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Knowledge Graph Insights

Larry Swanson

Interviews with experts on semantic technology, ontology design and engineering, linked data, and the semantic web.

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August 18 · 38 min
Eric Little: Super Domains, the Original Context Graph – Episode 56
Eric Little Knowledge graphs are complicated. They might entail multiple ontologies, several data sources, and any number of generated graphs and other context-establishing elements. Eric Little came up with the idea of "super domains" to understand and manage these important but ephemeral pieces of an enterprise's semantic knowledge architecture. A super domain might establish context that holds for just a few seconds for an ATM transaction or for several years for a clinical trial. We talked about: his recent move from Accenture to Knowledge3 as Chief Data Officer his diverse background in the consulting and enterprise worlds super domains, the conceptual framework he has developed to deal with generated graphs, reification, and other knowledge graph elements how super domains capture context some examples of super domains how super domains can help you deal with temporary, ephemeral, and stochastic data how his philosophy background led to his early exploration of the concept of context graphs the crucial distinction that needs to be made between epistemology and ontology in semantic practice the role of phenomenology in his conception and practice of ontology his take on the current state of semantic technology practice how generative AI has brought the importance of metadata to the fore and highlights the need for deterministic capabilities in AI architectures the dearth of tooling in the semantic space and the need to deliver "semantics at the speed of AI" Eric's bio Eric Little, PhD is Chief Data Officer at Knowledge3. He previously was Industry Innovation Principal Director & Head of Semantic Strategy & AI for Global Assets at Accenture. He received a dual Ph.D. in Philosophy and Cognitive Science in 2002 from the University at Buffalo, State University of New York. His Post-Doctoral Fellowship at the University at Buffalo’s Department of Industrial Engineering (2002-2004) focused on developing ontologies for multisource information fusion applications. He has worked in academia as a professor in several fields at several universities, as well as held multiple management & C-level positions in the software development industry across several different industry verticals. Having such a diverse background spanning academia & industry over the years provides Eric with a very unique set of experiences in the software development space that cuts across numerous disciplines and business verticals. After receiving his PhD and subsequently doing his post-doc, Eric has held various academic positions including Assistant Professor of Doctoral Studies in Health Education & Health Policy and founder of The Center for Ontology & Interdisciplinary Studies at D’Youville College. During this time he also started and ran his own consulting company which landed several high-profile customers across industries, including healthcare, insurance, oil & gas, and medtech. He is a world-recognized expert in semantic technologies, data fusion applications, data modeling, analytics, and AI. He has numerous professional publications in these areas, has been featured in industry publications, and is a well-known speaker at conferences around the globe. Before working at Accenture, Eric co-founded and was CEO of LeapAnalysis, the world’s first fully virtualized semantic search & analytics data science engine, which was named the #3 Most Innovative Data Science Company In The World by Fast Company Magazine in early 2021. He also was named Most Innovative CEO by Global CEO Magazine. He brings this knowledge and his passion for innovation everywhere he goes, developing new technologies and furthering the growth and success of his client base. Eric has a very simple goal in life – just change the world by making things no one has seen or thought about before. Eric is married and lives on the barrier island in Indialantic FL with his wife Jodi and their 2 cane corsos, Lemmy & Eddie. His daughters Gabrielle & Claudia are recent grads from University of WI Law School & Florida State University School of Business, respectively. For his personal life, Eric is a former semi-pro musician and is an avid guitar player & collector. He currently has a small digital studio at his house where he still writes and records songs (fun fact: his previous band, Satori, from the late 80’s-early 90’s is listed in The Encyclopaedia Metallum). He is also a motorcycle enthusiast and likes to tinker on his vintage bikes when time allows. Connect with Eric online LinkedIn Knowledge3 Photo of "super domains" slide in Eric Little's 2026 KGC presentation Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 56. A knowledge graph is a complicated thing. It might entail multiple ontologies, several data sources, and any number of generated graphs and other context-establishing elements. Eric Little came up with the idea of "super domains" to understand and manage these important but ephemeral pieces of an enterprise's semantic knowledge architecture — context that might hold for as little as a few seconds for an ATM transaction or for several years for a clinical trial. Interview transcript Larry: Hi, everyone. Welcome to episode number 56 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, Eric Little. Eric has most recently become the Chief Data Officer at Knowledge3. And welcome, Eric. Tell the folks a little bit more about what you're up to and your transition into this new role. Eric: Yeah, thanks, Larry. It's been a wild ride. It's been about a month now. Yeah, so I left my position at Accenture where I was leading a lot of our strategy practice around semantics for AI. But I happened to get back in touch with an old colleague of mine, Tom Plasterer, who I'm sure you and everybody knows. So Tom and I have had a long relationship. I mean, ironically enough, it's kind of funny, we're both actually from Wisconsin. Larry: Oh. Eric: So we're both Packers fans. He was born in Madison and grew up there. I was born in Green Bay and grew up there. So we come from kind of a similar Midwest background. We both root for the same football team. We both got into semantic scientologies. We've both worked a bunch in pharmaceuticals and life sciences, so it's kind of strange. We always refer to each other as sort of brothers from another mother kind of thing. And so Tom approached me about the company and it was a really great conversation and a good idea. Eric: And it really got me thinking because it was very similar to a company I had co-founded and was CEO of before called LeapAnalysis, which was a data virtualization company where we made Fast Company's number three most innovative data science company in the world in something like 2021, I think. But I went to Accenture. I've been there for about the last five years. Accenture was great, really good company to work for. Eric: Massive company as you know. And so now I've gone from something that's almost 800,000 people back to something that's like six people. So a little bit getting my sea legs there, but it's been really great. I sort of jumped in, started working with some of our clients already, and we're pushing the envelope on some of this stuff that we're doing in semantics, especially around things like virtualization, federation, and really serving up a semantic backbone for things like AI. Larry: Very cool. It's like what everybody's kind of doing now, stuff in that area. There's so much demand for this. It's really gratifying to see it. But I'm really curious about for you as a practicing ontologist and consultant, you were doing, as I recall, you were doing more kind of standard, not standard, but industry stuff and more like... Whereas knowing Tom and you and K3, are you focused on pharma and the life sciences in this new practice? Eric: We are currently, and a lot of that is because we're small and we want to be focused. So we both have a really strong background. Tom, probably stronger than mine even in this, as well as our CTO, Ivan, Ivan Stankov is another guy who comes from the AstraZeneca background. So those guys have been really, really deep into pharma. I had been a consultant in pharmaceuticals. I've been a consultant in medical device. I've also though been a consultant in everything like oil and gas, finance, consumer packaged goods. Eric: I spent a lot of my career in the defense industry. So I used to be one of these guys with a top secret SCI clearance doing a lot of three-letter agency work and stuff like that, building ontologies around threats and stuff for data fusion applications. But Tom and I had this talk and we decided, "Hey, look, we both have a pretty good Rolodex in the space. It's a good place for us to get started. We have a lot of deep subject matter expertise and knowledge in the space, so it makes a lot of sense." Eric: We're also both really, really into fair data, making data both findable, accessible, interoperable, and reusable. And those standards are really prevalent in the life sciences space. I mean, if you look around life sciences, there's something like 1,100 plus ontologies to grab in the life sciences space, whereas you go to finance and you have basically FIBO, and you go to something like supply chain and you've got one or two. Right? And you've always just got a couple of papers on things. Eric: But so I think it's sort of by design, but also through a product of necessity. But that doesn't mean we need to stay there. We do have desires to move eventually as we grow out of life sciences. But Knowledge3, as a technology, it's not bound to the life sciences at all. I mean, we could apply this to virtually anything, but you do need good heavyweight semantics when you are doing this kind of stuff in life sciences....
August 2 · 33 min
Dougal Watt: Human-Guided, LLM-Assisted Ontology Modeling – Episode 55
Dougal Watt Most conversations about taming generative AI focus on guardrails bolted on after the fact. Dougal Watt takes a different approach: generate the meaning first, as an ontology, and let everything else — agents, APIs, knowledge graphs — flow from that. His standing-room-only workshop on AI-augmented ontology creation at the Knowledge Graph Conference drew both business executives and semantic engineers, highlighting the broad interest in the need for accurate, trustworthy AI. We talked about: his work at the company he founded, Graph Research Labs and their meaning-first approach his very popular workshop on AI safety and ontologies and guardrails at the Knowledge Graph Conference the implications in AI architectures of the need to balance time, risk, and regulation the five main approaches that organizations are using to comply with regulations: guardrails multi-agent refinement governance frameworks sandboxing of agents ontology grounding how his Semantic Agent Harness delivers accuracy improvements better than even the best graph RAG systems how they build decision tracing into their framework the need to keep a human in the loop across the ontology-building process, as well as the crucial role of human judgement in automation workflows how LLMs can accelerate the task of integrating data in various organization silos how agentic modeling permits ongoing consistency checks on competency questions the need for more ontologists in AI work, but also tooling that he has created to facilitate ontology work how good ontology methods give organizations accuracy that is tailored to their unique knowledge Dougal's bio Dougal Watt is the CEO and Co-Founder of Graph Research Labs, inventor of multiple patents and patents pending covering ontology-driven declarative generation, governed AI agents, and automated enterprise stack creation. Previously IBM Chief Technologist and a global expert in information architecture. Open Group Distinguished Architect. IBM Certified Enterprise & Information Architect. TOGAF Certified. 30+ years across four continents building and fixing systems for some of the world's most demanding organisations. International speaker, most recently speaking about AI Safety and Guardrails at the Knowledge Graph Conference 2026 in New York. Connect with Dougal online LinkedIn Graph Research Labs Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 55. Generative AI gives ontologists powerful new capabilities that can help automate workflows and build and maintain ontologies. But as with any powerful technology you need wise guidance and sound methods to get the most out the tool. That's what Dougal Watt does. He has developed an approach that leverages the power of LLMs to accelerate ontology development while at the same time guiding every step of the process with human judgement and discretion. Interview transcript Larry: Hi, everyone. Welcome to episode number 55 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Dougal Watt. Dougal is the CEO and founder of Graph Research Labs in New Zealand. He's also the former chief technologist for IBM in New Zealand before he started his company. So welcome, Dougal. Tell the folks a little bit more about what you're up to these days. Dougal: Thanks, Larry, and thanks for having me on. Great to be here. Well, what I'm doing these days, I started Graph Research Labs to explore a really exciting intersection between information architecture, which has been part of my practice for a long time, information architecture, graph, and AI, and in particular using ontologies and graph to solve some of the really major problems that I've seen throughout my career. Those sort of problems they ... What we've seen over time is that most enterprise IT, it just becomes really focused on the past. You see layer upon layer of systems, often over decades. Each gets laid onto the last. And then organizations, they just end up spending more time, more effort to maintain that past than they are to build the future. And unfortunately, AI is making that worse, not better. So again, organizations are layering agents onto that same siloed set of foundations, and then they wonder why data's not accurate or governance just doesn't keep up and governance evaporates. Larry: Yeah- Dougal: So what we're doing, we flip that around at GRL. We focus on generating the meaning first. So instead of the meaning being an afterthought, we start from meaning and then we move into the data and then we generate that software stack. So the idea is that you shift to modeling your business once as an ontology, and then our declarative engine generates that whole stack for you. So you can generate the things you need, governed agents, APIs, apps, knowledge graphs, MCP servers, data products. You can generate all that in minutes. So that's been the focus. Larry: Very cool. And a lot of that ... There's so much in there. One, I love that it's messy and AI is not making it any better, but everybody I hear in Silicon Valley says, "No, we can just fix that." But that was one of the points of your talk at ... Oh, quick background. We met at the Knowledge Graph Conference last month where you did this great presentation on ... Was it AI augmented or LLM augmented ontology creation? I forget the exact title, but a really interesting presentation with a really well-developed workflow around how to use the capabilities of LLMs productively, but with really tight guardrails, human interventions, and things like that. Can you talk a little bit about ... Well, the other thing about that workshop is it was packed. I somehow got there early enough to get a seat, which I was grateful for. What motivated you to put that workshop together and why do you think it was so packed? Dougal: What motivated me is I saw this extreme focus on AI and then people needing to understand ... Well, I guess I'll take it back a bit. I think what was interesting to me is that it was so packed, and there was such a strong lineup of speakers at the conference in general, but I was really genuinely surprised how much interest there was in that workshop. And what we covered, we covered a lot of ground, but it's fundamentally about AI safety and ontologies and guardrails. So that was the focus. And I've never actually had a session before where you have to open up an overflow room and then people are sitting on the floor. It's kind of crazy. For a technical workshop, that's just really rare. Dougal: And I think this is one of the really interesting points is that a large number of people that were there, it might've been around 50-50, were senior executives, vice presidents, and their senior technical staff. And to me, that's a really strong signal that what's important to people is this AI safety and ontology issue and it's being driven from the top of the organization. And that's exactly what you want to see. Dougal: And I think as to why there's that focus on the ontologies, well, everyone's tried AI and they've tried different approaches, and they're finding that getting that into production is a real challenge because accuracy's not high enough. So if you're aiming for greater than 90% accuracy, whatever the use case is, you need an approach like ontology. So ontology seems to be the solution and people are looking for that solution, they're looking for tools, but obviously they're using AI, they're using LLMs. How do those two come together? How do they meet in the middle? How do we get value from this amazing new technology and it's just a technology? But how do we harness it, couple it, make it safe, and be able to drive the kind of value that we need in our organization so it can move into production and be accurate? Larry: Yeah, I think it's interesting. I remember even three years ago at the Knowledge Graph Conference that the budding, the looming talks about neuro-symbolic AI, hybrid AI, the combination of the two has been there. And it's really interesting that it took three years to get to matter-of-fact, like, yeah, this is clearly the place. And it's really gratifying to hear that there were business people in there as well, because it's not hard to get us excited about that. Dougal: We love it. Yeah. Larry: Yeah, executives. But one of the things that came up subsequently to that talk is one, that need for safety and guardrails and methods of taming these beasts is that there's this kind of disconnect between the insane acceleration and pace of change in the technology and the regulatory environment that drives a lot of the requirements for these things. Can you talk a little bit about that? Dougal: Yeah, we do a lot of work in that regulatory space, and I think the more I think about it, I think we're kind of at a really strange point in history. So we're sort of caught, like you say, this conundrum. It's between time, risk, and regulation. And so as society speeds up, the risk grows larger and then regulations are expanding exponentially. So again, the conundrum, that's making decisions slower. So in a sense, there's a impression, there's that disconnect between politics and regulators and technology instead of people actually working together to make life better and to harness this technology in the right way. Dougal: So the way I think of it as time speeds up, risk concentrates. And you can think of lots of examples of this. Trading algorithms can move a financial position in microseconds and it once took days to build that position. Or like money laundering, that chain can now move through accounts before the first alert fires off and people know about it. So what that says is that speed without accuracy, that's not progress,...
July 20 · 35 min
Yann Le Franc: From Semantic Silos to Shared Ontology Practice – Episode 54
Yann Le Franc The emerging field of neurosymbolic AI — combining LLMs and knowledge graphs in hybrid AI architectures — is new to a lot of people. For Yann Le Franc, it's the story of his career — from his academic days as a computational neuroscientist to his current work creating enterprise systems that ground LLMs in ontology-backed knowledge graphs. Over his extensive research and consulting career, Yann has honed his ontology design process, which he's now sharing in a platform that guides others in applying those time-tested ontology practices. We talked about: his work on the European Open Science Cloud, the LUMEN and GRAPHIA projects, and his KG and AI consultancy his academic evolution from cellular biology to neuroscience and then to computational neuroscience, neural network research, and neuroinformatics — and how that led to his discovery of the power of ontologies his current work to connect LLMs and ontologies a presentation on ontology-backed AI that he delivered at the Cultive Ta Data (Cultivate Your Data) conference earlier this year the refreshing emergence of the word ontology as a buzzword (as opposed to its "bad word" status only a couple of years ago) how he applies his research and innovation work to improve enterprise information systems his work to advance the practices of ontology design and knowledge graph building the inspiration he has taken from Robert Stevens to approach ontology development in a more aligned (non-siloed) way his ensuing adoption of the Linked Open Term methodology, an agile approach to ontology, and his broader work to reconnect "semantic silos" his journey into proper philosophically grounded ontology practice, and the notable absence of a canonical source of best practices the work at the LUMEN to develop a collaborative platform that guides practitioners to the tools available to each step in the ontology process how an enterprise ontology is the "skeleton of your whole information system, a representation of your company" — and how it accelerates BI and discovery and retrieval how much he's enjoying the return to neuroscience that comes with his work on neurosymbolic AI systems Yann's bio Yann Le Franc, PhD is the CEO and Scientific Director of e‐Science Data Factory S.A.S.U. Created in 2014, e-Science Data Factory is a French Innovation Center, aiming at proposing innovative solutions to transform data as key assets for industry and public organizations making their use of AI more efficient. The company leverages its participation into European Research Infrastructure projects to transfer knowledge and innovation to industry and public organizations. Yann Le Franc has a PhD in Neurosciences and Pharmacology in 2004. After a postdoctoral experience in the US, he worked on data management projects for Neurosciences in the context of the International Neuroinformatics Coordinating Facility (INCF) where he developed a strong expertise in ontology design and semantic web technologies. He then contributed to several Research Infrastructure projects aiming at building EOSC, the European Open Science Cloud (EUDAT, EOSC-Hub,…) as an expert on Semantic Web, ontology design and FAIR Principles. He is co‐chairman of the Research Data Alliance Vocabulary and Semantic Service Interest Group and the FAIR Mappings Working Group. He is the former co-chair of the FAIR Digital Object Forum Semantic Group and actively contributed to the EOSC Semantic Interoperability Task Force. He has been spearheading the FAIRification and standardization of semantic artefacts (i.e. ontologies, controlled vocabularies, …) and mappings/crosswalks in the context of FAIRsFAIR, OntoCommons and FAIR Impact projects. Through e-Science Data Factory, he is one of the co-founders of the Knowledge Graph Alliance. For the last 2 years and a half, Yann has been the Head of the EUDAT Secretariat, a pan-European e-Infrastructure, providing data management services, storage and computing services to European researchers. He is leading the creation of the EUDAT Node, one of the first nodes of the EOSC Federation. Connect with Yann online LinkedIn email: ylefranc at esciencefactory dot com Resources mentioned in this interview European Open Science Cloud LUMEN, GRAPHIA, and OPERAS EUDAT Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 54. One of the most interesting developments in AI architectures is the emergence of hybrid systems that integrate neural-network based LLMs with symbolic AI like knowledge graphs. For Yann Le Franc, this is very familiar terrain. He began his career in the neural network world as a computational neuroscientist but then quickly discovered the power of ontologies during a post-doc. He's delighted to see his interests re-converge in the current hybrid-AI era. Interview transcript Larry: Hi everyone. Welcome to episode number 54 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Yann Le Franc. Yann is, as you can tell from his name, he's based in France. He's in Montpellier. He's the CEO and founder at e‐Science Data Factory. He also heads the EUDAT Secretariat. He's the co-chair of the Vocabulary and Semantic Serviced Interest Group at the Research Data Alliance Association and he co-chairs the Fair Mapping Project. I have no idea how he found the time to talk with me with all that going on, but welcome, Yann. Tell the folks a little bit more about what you're up to these days. Yann: Yeah, thank you, Larry, for having me on the podcast. Well, as you've said, a lot of things are going on. We are working a lot on European research project to help build the European Open Science Cloud. And of course, we specialize on semantic web services, knowledge graphs, and the connection with AI. And in some of these projects, so the two projects that are currently going on are called LUMEN, and the other one is GRAPHIA. In LUMEN, we are developing a platform for industrializing ontology development and in GRAPHIA we're building a federation of knowledge graph for social science and humanities. Larry: Yeah, that's actually how we met. There was a conference in Zagreb last fall and put together by, I guess OPERAS, the overarching org, I guess that- Yann: The coordinator, yes. Larry: Yeah. And one of the first things I want to talk about is that I think it was there we talked about your journey from, you started as a neuroscientist and here you are this world-class ontologist. How did that transition happen? Yann: Yes, indeed. I started as a cellular biologist to be correct that specialized in neuroscience. And then I get my finger hooked into computational neuroscience. I was fascinated by the idea that you can simulate brain cells in computers. So move forward and keep going on working on, how do you say, simulating small neural networks, realistic biologically grounded neural networks. And then while during that time, I generated my own data and I had to deal with the data from my colleagues and I discovered a hell of working with data generated by others and even sometimes your own data. Yann: And I ended up after my postdoc in the US to actually join a team in Belgium in Antwerp and work for the international neuroinformatics coordinating facility based in Sweden. And there, my job was to actually develop an ontology for computational neuroscience models. And that's how I discovered the world of ontology and realized the power of it in terms of helping you structuring your data, sharing with others with the included meaning, which means the other understands what's in the data more easily. And of course, the connection to knowledge graph, where in the end you can start interlinking all your data together in a more simpler way and a more flexible way. Larry: Nice. We had a close call there. We could easily have lost you to the whole neural network community with that kind of background, but I love the story of how the need to understand and work with your data drove this. That's great. But that was a little while ago. So you've seen a lot of this. You've seen how neural network stuff is informing this, the benefits of ontologies and knowledge graphs in managing science data and understanding it better. And now we're all of a sudden, fast-forward to this year and we're three and a half years into this crazy new generative AI era. What's your kind of take on the current landscape? How has it changed I guess your interests and your work and your... Yann: We kind of see that coming with the development of very major AI. I don't remember the name (AlphaGo), but the one that actually won against the Go champion and all these big advances. And then we had the LLMs coming up, which is interesting. It's an implementation of neural network, gigantic neural networks. It's like trying to simulate the full brain. Unfortunately, it's not a brain definitely because it's just a statistical model. So it's interesting to see the democratization of the usage of the AI, at least the LLMs. It's a bit scary because I think the naming is wrong if we should keep calling them LLMs and not AI because there's nothing intelligent in these systems. It's just statistical models that gives you some interesting outputs that can emulate intelligence, but they don't understand the meaning. And that's also why I'm happy to go back to neuroscience and neural networks because now we are working a lot also on connecting knowledge graphs and ontology to LLMs to make them smarter because they are fantastic models for language. Yann: I'm using them also for helping me writing text in English because I'm French, so I'm writing in Frenchlish. So it's nice to have an LLM that helps me correct some of my writing but still you realize that sometimes the answers are comp
July 7 · 40 min
Ben Rode: Porting the Cyc Ontology to RDF – Episode 53
Ben Rode As the Cyc project winds down, one of its main contributors, Benjamin Rode, is attempting to migrate as much as possible of its upper ontology to RDF. It's an ambitious project, much like Cyc itself. Cyc arose from the early AI research community, and Ben has a fascinating perspective on that milieu as well. We talked about: his work to port as much as possible of the Cyc upper ontology into RDF the surprisingly long history of neuro-symbolic AI some interesting details and anecdotes about the origins of the field of AI the deep intertwining of the histories of symbolic AI and computing itself Doug Lenat and the origins of the Cyc project and the Cycorp company Lenat's concept of "white space knowledge," the framing for unstructured natural language text how an attempt to port the Cyc ontology to RDF is "not as insane an exercise as it might sound at first" some parallels and distinction between the RDF and Cyc worlds the unique characteristics and capabilities of the CycL language, in particular its homoiconicity the central question for the current era: "can these two frameworks (symbolic AI and probabIlistic AI) work together in a synergistic way" Ben's bio Ben Rode came to formal domain modeling and ontology engineering by way of Douglas Hofstadter’s Gödel, Escher, Bach: The Eternal Golden Braid, which he read in high school. His interest has since developed into study of machine learning, causal inference and induction, temporal reasoning, ontology evolution, and neurosymbolics. He holds a graduate degree in philosophy with philosophy of mind and analytic philosophy as areas-of-focus; the subject of his dissertation was the use of formalized contexts in common sense reasoning. He joined the technical staff at Cycorp in 1997, where he's played an active role in developing the Cyc ontology for a number of contracts, including extensive work on database schema integration, ontology extension and mapping, inference development, and domain knowledge acquisition from subject matter experts, in addition to assisting with research on using the Cyc ontology for LLM-assisted formal knowledge capture. His current research interests include translating a subset of the upper Cyc ontology into RDF, large language model-assisted knowledge graph extension, and the use of knowledge bases for validation and verification of large language model output. Connect with Ben online LinkedIn email: benjamin dot paul dot rode at gmail.com Resources mentioned in this interview Cyc Automated Mathematician Heuretics: Theoretical and Experimental Study of Heuristic Rules Eurisko Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 53. It turns out that contemporary explorations of hybrid AI are actually re-opening conversations that started almost 70 years ago. In the early days of AI, neural network "connectionists" and symbolic AI researchers saw their work as naturally complementary. Out of that primordial AI ecosystem emerged Doug Lenat's Cyc project, an ambitious effort to account for all of humanity's common-sense knowledge. Ben Rode is now trying to bring that work to the RDF world. Interview transcript Larry: Hi, everyone. Welcome to episode number 53 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, Ben Rode. Ben is a longtime ontologist, best known for his association with the Cycorp project, and we'll talk a little bit about that and the Cyc Project in general. He's also working on, and the reason I wanted to have him on the podcast today, he's working on a project to port the Cyc attempt to account for common sense knowledge in the world into the RDF world that most of us are familiar with. So welcome, Ben. Tell the folks a little bit more about what you're up to these days. Ben: Okay. So in the course of finding my way to a new position, I'm working on several projects of my own. One of them, as you mentioned, involves trying to port as much as can be ported from the upper Cyc ontology, particularly the 2014 version of that that is in the public domain. It's available under public licensing on GitHub, to try to port as much of that as possible into the Resource Description Framework and the semantic web. And this has actually become an experimental interest of mine and I think it may be we can learn quite a lot from doing that. Larry: Yeah, that's a good opportunity to remind me to ask you about the history of all this. And it goes back, you did a presentation a while back that I saw, where you set out the overall history of the development of the field of AI and how Cyc and other things fit into it. Can you talk a little bit about that? Ben: Yeah. Well, I mean, I think the framing context there is partly the question of, well, why would you want to translate the upper ontology of Cyc into RDF and what would that be good for? And perhaps there's even a larger question there of what would Cyc or the semantic web be good for in the current AI context? Now, I mean, I think the story here really is maybe much older than a lot of people realize. I mean, the roots of AI generally go back quite far. And here I think it's very important to distinguish between what we sometimes hear referred to as symbolic AI versus sub-symbolic. I think both the semantic web technology stack and Cyc can fairly be described as symbolic AI. Large language models are a good example of sub-symbolic AI, sometimes referred to as generative or probabilistic AI. Now, I mean, it's really interesting that this really starts... For symbolic AI, it's really quite early. I mean, it starts back at the end of the 19th century really with what we might term as math validation. Ben: What you have here is a set of concerns coming up in the mathematical community that the foundations of mathematics might not in fact be sound, that there might be hidden contradictions. And certain developments, spearheaded, for example, by Bertrand Russell, it led people to believe that the worst fears might be realized. So what you have developing in the late 19th and early 20th centuries is an effort to put mathematics on a sound foundation. And one of the things that comes out of this, not only is it symbolic AI, computing itself and digital computers and programming really have their origins within this thread. And then a little later than that, really starting up maybe in the early 1940s, you have information theory, which is coming with Claude Shannon and then associated with that is a movement that has been almost completely forgotten now and I think regrettably forgotten. Ben: It was called Cybernetics. If I remember right, the name originated with the Greek, hopefully I'm pronouncing this right, kybernētes, meaning steersman. I mean, the interest was in homeostatic systems, our systems where there was re-entrancy of information and energetic cycling to maintain homeostatic conditions. And there was a belief that this could tell us a lot of things, physics, biology, information, science. Some of the key names there are Norbert Wiener, Heinz von Foerster. Let's see, I think it was, I believe Humberto Maturana and Francisco Varela and also a guy named Hans Jonas, who I've only recently found out about, and I'm starting to believe, beat everyone to the punch on that. But this is the thread that in some ways gives rise to generative AI. I mean, it comes through things that were called perceptrons that were pioneered by Warren McCullough, his student Walter Pitts. Ben: But an interesting aspect of it is that when you go back and look at some of these writings, particularly Norbert Wiener and Claude Shannon, one of the things that really comes up is they were talking in terms of complementarity. They were talking in terms of what we would call symbolic and sub-symbolic AI complimenting each other. Shannon was talking, for example, about auto-tuning versus code optimization. And if you think of auto-tuning roughly as being the machine learning, generative AI end and code optimization being the symbolic end, those were seen as being mutually supportive. And I think that is a feature we really want to... We're at a point where we need to be reexamining that. I mean, it is being reexamined under the heading of very... We hear about hybrid systems or neurosymbolic AI and this really is it. We can talk some about what are the comparative strengths and weaknesses of symbolic and sub-symbolic AI in a little more detail if you like, but I think we- Larry: I love that too, that everything you just said goes back, because virtually everybody else I've talked to kind of marks that 1956 Dartmouth Conference as the dawn of AI. Well, that's where McCarthy coined the term AI. Ben: That's where McCarthy coined the name, yes. Larry: Yeah. But also it just occurred to me like, duh, that didn't just come out of nowhere. And in fact, I talked to our mutual acquaintance, Pat Hayes, about the origin of that and he said, "Yeah, the whole notion, that coining of the term artificial intelligence was to distinguish it from cybernetics, to distinguish-" Ben: Well, no, exactly. And I mean, McCarthy at the time, 1956, he was sort of the young Turk and Norbert Wiener was the gray beard. Again, there may be people in the audience who know much more about this than I do, but based on the accounts I have read and what I have heard, McCarthy was kind of concerned that if Wiener was in that conference, Cybernetics were going to dominate and he didn't want that to happen. He wanted it to be about symbolic AI. So folks, I mean, not only Wiener but McCullough and Pitts were kind of cut out of that discussion. And in some ways that may have been a little unfortunate. I think the history might've been very different if there had been more interaction and more
June 9 · 34 min
Lulit Tesfaye: Semantic Architectures for the AI Era – Episode 52
Lulit Tesfaye Generative AI has prompted a flurry of experimentation in enterprises, resulting in a parade of failed pilots, unproven PoCs, and unrealized return on investment. Lulit Tesfaye helps companies improve and optimize their AI capabilities by adding semantics to their enterprise architectures. This puts their precious knowledge assets in a context that machines can actually work with. A crucial part of that context is the interoperability that standards like RDF enable. We talked about: her work at Enterprise Knowledge as Partner & Vice President, Knowledge, Data, and AI Solutions the publication of Bridging Knowledge, Data, and AI: Harnessing the Semantic Layer Framework to Drive Intelligence, which she co-authored with colleagues at EK her professional path from software engineering and applications development to her current role how generative AI has increased data teams' workloads, leading to numerous pilots and PoCs, which has exposed the need in enterprises for semantic context her definition of a semantic layer "knowledge assets," a foundational concept that she uses to talk about data, content, information, expertise, and any other assets to which can append metadata how she and colleagues see the distinction between the two types of semantic layers: data and analytics layers and semantically modeled layers, and the benefits of the latter the framework she uses to convey the benefits of a semantic layer to executives the swing of the enterprise architecture pendulum back towards monoliths, as enterprise solutions providers like ServiceNow acquire semantic tool companies the importance of interoperability standards like RDF in semantic systems examples of use cases that are best suited to labeled property graphs and RDF knowledge graphs and how she sees the debate about which to use going away soon two common failure points in semantic layer projects: starting with tools, not competency questions insufficient skillsets and lack of leadership support the use of multiple levels in a semantic layer in large, complex organizations emerging trends she sees in semantic architectures: the ubiquity of AI the need to design for machines the need for investment in a context layer the disappearance of the artificial lines between structured and unstructured knowledge assets the evolution of enterprise operating models the importance of staying focused on our knowledge assets and adopting solutions that are based on standards and enable interoperability Lulit's bio Lulit Tesfaye is a Partner and the Vice President at Enterprise Knowledge, LLC., the largest global consultancy dedicated to knowledge and data management. Lulit brings over 15 years of experience leading global initiatives, specializing in information and data management solutions and integrations. Lulit is most recently focused on applying practical knowledge management, data governance, and semantic data foundations to optimize organizational information assets – providing the structure, context, and explainability needed to support responsible and effective enterprise AI. Connect with Lulit online LinkedIn Enterprise Knowledge Video Here’s the video version of our conversation: https://youtu.be/E6Yz6GEop7M Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 52. Knowledge management and data engineering were already changing even before ChatGPT arrived. Now, with the proliferation of AI, the boundaries are blurring between structured and unstructured data and other knowledge assets. As a partner at a prominent knowledge consultancy, Lulit tess fai sees every day the implications of this change, chief among them the need for semantic layers built for interoperability on a solid foundation of industry standards. Interview transcript Larry: Hi everyone. Welcome to episode number 52 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Lulit Tesfaye. Lulit is currently the Partner & Vice President, Knowledge, Data, and AI Solutions at Enterprise Knowledge, the big famous consulting agency in the DC area. She's also the co-author of the recent book, or maybe forthcoming, I'm not sure, but I've seen it online so I know it's coming. Bridging Knowledge, Data, and AI: Harnessing the Semantic Layer Framework to Drive Intelligence, which she wrote with her co-authors, her colleagues at EK. Well, welcome, Lulit. Tell the folks a little bit more about what you're doing these days. Lulit: Thank you, Larry. Thanks for having me. I'm happy to be here and have been looking forward to this conversation. That's a very kind introduction, so I appreciate that. So, maybe a good way to start would be a little bit from the journey over the, without dating myself, I'll say I've been in this space close to 20 years at this point and it's been not a direct or linear path given how things are evolving, but the short version of it is started in working within software engineering and applications for data and information in that space, which led to me joining Enterprise Knowledge after I met the co-founders about a decade ago. Lulit: And then our focus was in knowledge management primarily, which traditionally is considered as learning and development or training or relegated to librarians, so to speak, information architecture and so forth. But over the past five years specifically, we've been seeing a big shift towards the silos in a way dissolving between the different types of these groups within organizations and where information or knowledge sits. So, we think in terms of structured and unstructured data, unstructured being content, PDFs, and all the things that you have in your files and folders, whereas structured data has been traditionally for data and analytics teams. Lulit: And the biggest swing that we have seen thanks to especially generative AI, is that unstructured data, which is really 80%, 90% of your organizations, I'll call it knowledge asset, came into the purview of data teams, data and analytics teams because of generative AI. And that was what led to this big focus on knowledge management for data as well as handling structured data. So, I think we've all gone through the pilot phases for POCs and pilots for AI with the pilot purgatory, all the failed efforts. That has been the journey, especially over the past, I would say five years is working with organizations that have been experimenting and embracing AI or today trying to wrangle what we call the AI pilot sprawl, scenario that exists. Lulit: So, to give you an example, one of our clients recently said we just cataloged over 150 pilots, AI pilots, and we haven't seen the return on investment as we should. Good experimentation, good learning, but we need to put an end to it and have a strategic path. So, this is where we are today. All the pilots and POCs for many different reasons that I can get to either have failed or have stalled. And at the same time, there are a lot of things in production that I would commend the organizations leading that. But the biggest failure and a big part of our journey has been the backbone, the core, the semantics or the context for AI as the lens that AI sees your organization, that has been the core of it. Just to answer maybe in a long way what we've been working on over the past few years. Larry: Yeah, I think the first time I met you, you did a talk about the semantic layer at that data summit in London, the one that Henry Stewart Events puts together. And I've been following your work about semantic layers since then. And what you just talked about, that's kind of the classic sort of framework that people use to connect this, like the data stuff you're talking about, the KM. But I'm really curious, in the knowledge graph world, everybody talks about knowledge engineering. And what you just described seems like the setting the stage for a big shift from knowledge management to knowledge engineering. Does that make sense? And if so, how does it fit in how you see with things right now? Lulit: It does. And I think maybe the best way to answer that would be to take a step back and really define what we mean by semantic solutions or specifically a semantic layer, because I think there's a lot of confusion in this space. It's funny, we start the book with it's all about semantics, right? It is truly about semantics. And the key definition that I would make here distinction is semantic layer is not something new. In fact, we're just chatting. It celebrated its 25th year anniversary this year. It's come a long way I think. And what we mean by semantic layer is we are talking about the shift from the focus from the physical data itself to the data about the data, so the meta aspect of it. Lulit: So, this means abstracting the physical data with definitions, business glossaries, metadata, the aboutness of the data, taxonomies to control some of these vocabularies that are very unique to your organization, what fits under our product, list of product, list of services, ontologies to be able to relate those concepts, these vocabularies that you have to your real world entities. This customer owns this product, has bought this product and needs this type of marketing material or training material. How do you connect to these pieces? That's where ontology allows you to explicitly make that machine-readable, places things. Lulit: And then when you apply this model, this semantic model on your assets, and I am collectively going to call going forward data content and information and expertise, anything you can append metadata to, knowledge assets, then you have your graph, your Knowledge Graph. So, this is what we mean when we talk about a semantic layer and the components that sit within semantic models....
May 25 · 32 min
Giancarlo Guizzardi: Ontology, Semantics, and Explainable AI – Episode 51
Giancarlo Guizzardi For nearly three decades, Giancarlo Guizzardi has researched and advanced the field of semantics and the practice of ontology and conceptual modeling. His work on the Unified Foundational Ontology (UFO), the OntoUML pattern language, and AI explainability are just a few of the accomplishments that make him an exemplar of the "full-stack ontologist." We talked about: his broad-ranging ontology and other responsibilities work at the University of Twente in the Netherlands the origins of the term ontology in computer science in 1967 George Mealy's assertion that "every data makes an ontological commitment" his take on the idea of capital O Ontology, both the conceptual tooling to build ontologies as digital artifacts and the design patterns that guide their creation how his insight that conceptual modeling is the foundation of any system led to his development of the Unified Foundational Ontology (UFO) his goal with UFO to give engineers tooling to reuse ontology patterns without having to expose them to the complexity of the underlying ontology itself the resulting OntoUML pattern language his belief that ontology engineering should separate conceptual modeling from design and implementation his take on the difference between verification and validation in ontology design how conceptual modeling and engineering implementation often end up in the hands of a "full-stack ontologist" how the ideas in his paper on "Explanation, Semantics, and Ontology" support explainable AI Giancarlo's bio Giancarlo Guizzardi is a Full Professor of Computer Science the University of Twente, The Netherlands, where he chairs the Semantics, Cybersecurity & Services (SCS) department. He is also a co-founder and co-director of the NeXAI Competence Cluster in the same university. He has been active for nearly three decades in the areas of Formal and Applied Ontology, Ontology Engineering, Conceptual Modeling, Enterprise Computing and Information Systems Engineering, working with a multidisciplinary approach in Computer Science that aggregates results from Philosophy, Cognitive Science, Logics and Linguistics. He is the main contributor to the upcoming ISO/IEC international standard 21838-5 Unified Foundational Ontology (UFO) and to the OntoUML modeling language. He is an associate editor of several journals including Applied Ontology and Data & Knowledge Engineering, chair of the Steering Committee of the International Conference on Conceptual Modeling (ER), member of the Advisory Board of the International Association for Ontology and its Applications (IAOA), and an ER fellow. Finally, he has extensive technology-transfer experience developing industrial ontologies in sectors such as Health, Cybersecurity, Risk Management, Space, Finance, Energy, Distributed Software Development, Digital Journalism, Complex Media Management, Government. Connect with Giancarlo online LinkedIn GiancarloGuizzardi.com Resources mentioned in this interview Another Look at Data, George Mealy's 1967 paper Explanation, Semantics, and Ontology Ontology, Ontologies and the “I” of FAIR Unified Foundational Ontology (UFO) OntoUML Video Here’s the video version of our conversation: https://youtu.be/JtsC8nQNFF0 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 51. The origins of the practice of ontology in computer science go back almost 60 years, well before the current era of knowledge graph technologies. Since then, ontology researchers like Giancarlo Guizzardi have demonstrated the importance of distinguishing between conceptual modeling and the symbolic language that implements the model. Giancarlo's latest work shows that genuinely explainable AI is impossible without formal ontology and semantics. Interview transcript Larry: Hi everyone. Welcome to episode number 51 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Giancarlo Guizzardi. Giancarlo is a professor at the University of Twente in the Netherlands. Welcome, Giancarlo. Tell the folks a little bit more about what you're doing these days. Giancarlo: Hi, Larry. Thanks for having me. It's a pleasure to be here talking to you. As you said, I'm a professor here in the Netherlands. I'm the head of a group called Semantics, Cybersecurity, and Services. So as the name says, everything we do is grounded on semantics and ontologists. And we call ourselves full-stack ontologists. Giancarlo: So we work from very theoretical issues. So sometimes I even publish in philosophy journals, to very practical issues of... So we go from building ontologies in philosophy, ontology in computer science, modeling languages, tools, ecosystems of tools for ontology engineering, and to implementation of ontologies in large-scale settings. So we've been doing this for quite a while in many different domains. Giancarlo: The group now is very focused on cybersecurity, on risk management and on social and legal issues. So the service part refers to that. And it's a big group, around 70 people here in the Netherlands. Larry: Oh, wow. I didn't realize it was that big. Well, and that scope that you described, and I love that you describe yourself as a full-stack ontologist because there were a number... I just came back from KGC and there were a number of presentations there that they take the semantic layer and divide it into five or six layers of its own, a lot of which aligns with what you just said. Larry: But one of the things you talk about, and I think it fits into this, with this deep varied ontology practice spanning a bunch of different domains, different levels from the highbrow ontology stuff to the in-the-weeds data stuff. You argue that capital O, Ontology, like a proper philosophically grounded... Or I don't know exactly what you mean by that, but tell me more about what you mean by capital O, Ontology, and why it's essential for engineering practice? Giancarlo: Yes. Ontology, capital O, is basically... So the term refers to three different things, ontology. It refers to, originally in philosophy would refer to a particular theory about a given domain. So what exists in a given domain? So one interpretation is what exists behind a certain description? The ontologist, whatever, a certain description assumes to exist in the world in order for that to be true. So we can see how that connects with data. Giancarlo: So there is a quote that I like very much from a guy called Mealy. Mealy is the creator of Mealy Ontology in computer science. He was the PhD supervisor of Peter Chen, the guy that created entity relationship diagrams. So grandfather of conceptual modeling. Mealy writes this paper in 1967 called Another Look at Data, which the first reference of the word ontology in computer science, by the way. So we are talking about ontology in computer science since '67. Giancarlo: And Mealy has this nice quote. He says, "data are a theory, a fragment of a theory of the real world." So he's saying every data makes an ontological commitment. This is absolutely inevitable. In fact, any type of representation makes an ontological commitment. So even if you have a kind of Python code that has variables like customer and purchase order and product and so on, you are committing to a given theory of the world of what kinds of things exist with which properties, under which constraints, and so on. Giancarlo: And Mealy makes his reference to ontology basically saying, we need to make that explicit, interoperability in a sense. So he's not using these words, but he's saying computer scientists are obsessed with symbols and with symbol manipulation, but we need to look beyond the symbols at what kind of theory of the real world is behind that symbolic structure. Giancarlo: So this is a definition of ontology, it's whatever is behind a symbolic structure. In computer science in another area in AI, particularly in the end of the '70s with Pat Hayes and so on, ontology became the structure itself. So the representation of that theory in the background. So these are two interpretations of ontology. Ontology as whatever is in the background, whatever is assumed by a representation, or the representation itself. Giancarlo: Well, the representation only justifies its name if it's a representation of the ontology in the background. Otherwise, we could just call this data structure or data model. Ontology, capital O, is a area that allows you to, let's say, flesh out, review, make explicit what is the theory of the real world, which is behind a certain symbolic structure in a systematic way. Or in other words, Ontology, capital O, is an area that will give you instruments, conceptual tools to build ontologies as artifacts. Otherwise, we're going to need to invent these conceptual tools. Giancarlo: So typically Ontology, capital O, will deal with the most general aspects of reality, like what are events, what are objects, how objects relate to their parts, how events relate to their parts, what kind of properties can objects have, what kind of relations can connect objects and so on and so forth. What kind of types exist, how types relate to each other forming taxonomic structures, very general theories. Giancarlo: So I see these theories as kind of not only conceptual tools, but actually as kind of patterns. So let me give you an example. So imagine if you have a theory of events that says an event is something which happens in space and time, events have parts. So there is a mirror logical structure in events of events. These parts can be related by different types of temporal relations, by causal relations, objects participate in events. So events are kind of dependent entities. In order for them to exist something needs to participate in this event. Giancarlo: So you have this theory,...
May 11 · 35 min
Ora Lassila and Adrian Gschwend: RDF 1.2 Working Group Update – Episode 50
Ora Lassila and Adrian Gschwend Even as RDF has become ubiquitous in enterprises and across the web, its awkward handling of reification — the ability to refer to other statements in a graph — has limited its wider adoption. RDF 1.2 addresses this with the reifier: a new element that lets you attach provenance, confidence, and source directly to a relationship — including claims you're tracking but not asserting as true. We talked about: Ora and Adrian's extensive experience and backgrounds in the RDF community how the need to better handle reification led to the development of RDF 1.2 the W3C's use of the term "recommendation," which many/most people would think of as a "standard" the recent advancement of the RDF Concepts and Abstract Syntax and RDF Semantics specifications to "candidate recommendation" status the new RDF triple term - and how it permits references to RDF statements that have not been asserted some of the details that made reification difficult in prior versions of RDF: verbosity, inability to scale, etc. how the ability to reason on data distinguishes RDF graphs from labeled property graphs the high quality of the RDF 1.2 W3C working group and their confidence that their work has accounted for all of the important considerations that might arise the challenges of dealing with the needs for both backward and forward compatibility how committee specifications like RDF 1.2 compare with less collaborative vendor specifications how RDF saves BMW millions of lines of code when reasoning over car features how standards-setting has evolved over time, from codifying existing practices 30 years ago to more proactive approaches today Adrian's appreciation for the working group volunteer contributors and how they exemplify the values of open standards, open source, and open data Ora's observation about the truly open and transparent nature of the working group and the many benefits open standards, including the ability to avoid vendor lock-in Ora's bio Dr. Ora Lassila has been working on the Semantic Web since 1996, first exploring possibilities for knowledge representation on the Web—work that launched the W3C RDF activity—and later pursuing his ideas about using autonomous agents on the Web—something that became the original Semantic Web vision as articulated in the 2001 Scientific American article he co-authored. All this was preceded by several years of research work on knowledge representation, ontologies, agents, planning, and other classical AI technologies. He is currently an Associate Director of Data Engineering and Governance at Accenture, working on topics like ontologies and knowledge graphs. He is also the co-chair of the current W3C RDF & SPARQL Working Group that is defining the next version of the RDF standard. His prior positions include Principal Technologist (in the Neptune graph database team) at AWS, Managing Director (Head of Ontology Engineering) at State Street, Research Fellow (Head of Agent Research) at Nokia Research, and Project Manager at Carnegie Mellon University, among several others. Dr. Lassila’s knowledge representation software flew onboard the NASA Deep Space 1 probe to the Asteroid Belt in the 1990s. He is also a Grand Prize Winner of the Obfuscated C Code Contest. He received his Ph.D (D.Sc) and M.Sc degrees at the Helsinki University of Technology. Connect with Ora online LinkedIn Adrian's bio Adrian Gschwend is the founder of Qlevia AI, an operational knowledge platform for enterprise AI, designed to help organizations turn complex, evolving data landscapes into reliable, real-time systems. For more than a decade, Adrian has focused on making knowledge graphs scale, both technologically and in real-world applications. As an engineer, he has worked hands-on with enterprises and public institutions to solve complex data integration challenges, building systems that reflect how businesses actually operate and evolve over time. Adrian has a strong background in open source and open data, contributing to large-scale government platforms in Switzerland and Europe, as well as working with organizations such as BMW. He also serves as co-chair of the World Wide Web Consortium RDF 1.2 Working Group. His perspective is that while AI has advanced rapidly, most organizations still operate on fragmented and disconnected data. He is focused on closing that gap by building systems where data, context and decision-making come together into a reliable operational layer that adapts with the business. Connect with Adrian online LinkedIn email: adrian at qlevia dot com Resources mentioned in this interview RDF 1.2 Concepts and Abstract Data Model RDF 1.2 Semantics The Semantic Web, Scientific American, May 2001 Video Here’s the video version of our conversation: https://youtu.be/VlRsTyHDdY8 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 50. The standards that make the World Wide Web work are built on the volunteer labor of experts like Ora Lassila and Adrian Gschwend. Ora and Adrian co-chair the working group that is bringing a powerful new capability to the W3C RDF standard. In previous versions of RDF, reification has been a verbose and complex process. RDF 1.2 introduces a new rdf:reifies property that simplifies and streamlines the ability to refer to other triples in a graph as first-class objects. Interview transcript Larry: Hi, everyone. Welcome to episode number 50 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Ora Lassila and Adrian Gschwend. Sorry, Adrian. My German is horrible at this point, but Ora's just started a new job at Accenture. That's really exciting. Maybe we'll talk a little bit more about that. Most people know him as like a longtime Neptune person at AWS. Adrian is the also well-known long time at Zazuko and now is the CEO at Qlevia. Anyhow, welcome to both of you. Maybe Ora, start with you. Tell the folks a little bit more about what you're up to these days. Ora: Yeah, thanks, Larry. Well, you pretty much said the important part, I'm just in the process of having switched jobs, but I am also the co-chair of the RDF and SPARQL Working Group at W3C, and have been that for quite some time now. That's, I guess, the important part here. Of course, I've had long history with RDF and started this whole thing back in the late '90s. Larry: Yeah, that's a little bit of an understatement to say that you've been involved a little while, but we can talk more about that later. Adrian... Adrian: Thank you, Larry. Happy to be here as well. Yeah, so well known probably for Zazuko, for a lot of the open source tools we do in the RDF knowledge graph domain. Some people know me for Qlever and QLeverize as well, where I'm a chief commercial officer right now. Qlevia, is basically my try to not have to talk about graphs anymore, so I want to scale the technology but solve problems, and for the first time venture funded, so that will be the next hopefully fantastic years. Larry: Oh, that's exciting. Looking forward to hearing more about that, and I neglected as I introduced you, the reason you're here is that you're co-chairs of this committee. Adrian: True. Larry: Yeah, so one of you talk a little bit about, and maybe Ora, the history of the project and how the need to upgrade to 1.2 came about. Ora: Right. Well, so we go all the way back to the first version of RDF. In the late 90s, it introduced something that we call reification, which basically lets you talk about RDF statements, so RDF graphs are all about statements, where you say things like, "Adrian's nationality is Swiss." That would be like a statement, but then sometimes you need to talk about the statements themselves. So if, for example, if I wanted to say, "Adrian believes that the moon is made of cheese," I don't necessarily want to say the moon is made of cheese, but I want to talk about the fact that Adrian might think this way. And so, reification was a mechanism to accommodate something like this. Interestingly, if you look at the draft versions of the first RDF specification, the reification kind of started out fairly close to the top of the specification, and in consecutive version, it moves further and further back. I always think that, once it got published, it became sort of the most misunderstood and most hated part of RDF in many ways. Ora: It's a little cumbersome and misunderstood, because I think people took some of the stuff too literally, but over the years, people have suggested various ways of "fixing this," and a little over 10 years ago, there was a paper called Reification Done Right that was written by a couple of my former AWS colleagues. That got people sort of reengaged with the idea of reification really should be fixed somehow, and it turned into a community group at W3C, called RDF Star. Community groups at W3C have this kind of like a lightweight process. They cannot produce specifications. They can only produce final reports, which can then be input to working groups at W3C, which can be chartered to produce actual WTC recommendations. When I say recommendation, for those listeners of yours who don't know, recommendation is the term that W3C uses for something that some other organizations might call a standard. I think that sort of originally the term implies that W3C has no enforcement authority of any kind. People implement the recommendations if they think that they're a good idea and that they promote interoperability. Larry: Interesting. I always wondered, because the authority before, as a candidate recommendation, I always thought that was kind of odd language, but that explains that. Ora: Right, so the end goal here is to produce something or publish something that's called a recommendation,...
April 29 · 38 min
Daniel Davis: Grounding Generative AI with Context Graphs – Episode 49
Daniel Davis Long before Foundation Capital published their "trillion dollar opportunity" article about them, Daniel Davis had been building a platform for context graphs. Daniel's work in complex domains like aircraft safety and autonomous vehicles, as well as his study of quantum mechanics, gave him insights that led him to explore ways to ground probabilistic AI systems in the logic and knowledge they'd need to deliver trustworthy information. He settled on context graphs as the best way to accomplish this. Daniel was introduced to knowledge graphs by his co-founder Mark Adams, and he has immediately become an RDF evangelist, aiming to not only proselytize the tech but to also make Mark's cat Fred famous in the process. We talked about: his role as co-founder at TrustGraph his work to make his co-founder Mark Adam's cat Fred famous his diverse background in defense, autonomous vehicles, and cybersecurity how the complexity and vast scope of compliance requirements around autonomous vehicles led to his interest in context graphs how the arrival of ChatGPT and GPT-3, and his knowledge that probabilistic systems wouldn't be up to the task of delivering legally compliant information, served as a catalyst for his current work how a friend's article about the Foundation Capital "trillion dollor opportunity" post led to his Context Graph Manifesto his hypothesis, based on conversations with several friends at big consultancies, that the sudden interest in context graphs arose from executives reviewing their many failed 2025 AI proofs of concept his definition of a context graph: "a graph structure that is optimized for AI usage" the influence of his friend Vicky Froyen's 2019 presentation on context graphs at the first Knowledge Graph Conference the three elements he sees in a context graph - decision traces, provenance and explainability, and feedback - and the power of combining them in a single graph system their use of ontologies like PROV-O the importance of a context capability in complex domains like military airworthiness how his background in quantum mechanics and mathematics led to his awareness of the limitations LLMs from their introduction how he balances the probabilistic nature of the universe with the needs of practical applications that entail legal obligations his surprise at the lack of attention that a lawsuit between Amazon and Perplexity is getting, given its huge implications for AI agent systems their goal at TrustGraph of making graph technology and ontology design easier and more accessible a cliffhanger about the implications of LLMs not understanding time Daniel's bio From military aerospace, space-to-air-to-sea mesh networks, autonomous vehicles, and enterprise infrastructure, Daniel has made of career of making the most complex systems work together. Whether it's cyberphysical systems or data, interoperability and guaranteed performance have always been top priorities with a mission-first mindset. Co-Founding TrustGraph represents a multi-decade quest to improve decision making through access to better knowledge. Connect with Daniel online LinkedIn X Resources mentioned in the interview TrustGraph.ai TrustGraphAI YouTube channel Context Graph Manifesto Collibra's Context Graph, Vicky Froyen's 2019 Knowledge Graph Conference presentation Video Here’s the video version of our conversation: https://youtu.be/npjErvR7oXY Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 49. When Foundation Capital published their article about the trillion-dollar opportunity presented by context graphs, many people were hearing about the concept for the first time. Not Daniel Davis. He's been developing an open-source context graph platform since 2023. His work in complex domains like aircraft safety and autonomous vehicles, as well as his study of quantum mechanics, have led him to explore ways to ground probabilistic AI systems in logic and knowledge. Interview transcript Larry: Here we go. Hi everyone. Welcome to episode number 49 of The Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Daniel Davis. Daniel's the co-founder and co-creator at TrustGraph, which is an open-source software project that builds graph stuff that we'll talk about today, based in San Francisco. And welcome to the show, Daniel. Tell the folks a little bit more about what you're up to these days. Daniel: Oh, wow, Larry. That's a lot to unpack there. I mean, how much time do you have? Yes, I am the co-creator of TrustGraph with Mark Adams, who is a bit more well-known in the graph community than me, but he likes building graphs. He doesn't like talking about them so much. And I'm confident that he would agree with me on that. Although I am trying to make his cat Fred famous, because I'm actually working on a new video on our guide to understanding RDF, which is something that a lot of people have asked us about, and how Mark taught me RDF so many years ago with three simple sentences about his cat Fred. But TrustGraph is what we've been working on for the past few years now. And we've had a couple of different ways of trying to explain it to people, whether it's a context operating system, context development platform. Daniel: Some might even think of it like a context science platform, which I think is kind of an interesting analogy as well. But I myself have quite a diverse background, spent a lot of time in DOD aerospace, came out to Silicon Valley almost 10 years ago to work on the autonomous vehicle industry, focusing on cybersecurity and safety. And that's why I write articles about things like determinism and information risk and trying to attribute value to information. But in that world, I also was doing complex knowledge work where you read one document that's 800 pages long, and then you have to read a statement that references another document, or maybe it references 12 other documents, and you just keep tracing down this chain of references, and then you have to understand which one of these documents actually takes precedence. Why did these statements conflict with each other? Daniel: Do they conflict with each other? How do I try to come to some sort of opinion about this? And in the safety critical world, opinions aren't allowed. It's not like auditing for enterprises that you can have opinions. They take a much grimmer view on that. And that's where that word determinism comes in and whether determinism means what people think it means. And how is that for an introduction? Larry: Well, it's perfect, because it sets up all the things we want to talk about. The first thing I want to talk about, I think, well, it's so hard to choose, but the reason you came to my attention is, I forget, somebody ... Oh, my friend Jochen in Munich brought you to my attention. And I was like, "Whoa, this guy's been talking about context well before December of 2025," which is when apparently the rest of the world started thinking about context and context graphs. Tell me a little bit about maybe the story of your connecting with Vicky or however. I mean, that combination, we were talking before we went on the air about your experience with autonomous vehicles, discovering Vicky and his interest in context graphs. And then a lot of what you just said is a reason to need not the context graphs to do the stuff you want to do. So, maybe talk a little bit about your journey into the context realm. Daniel: Well, so much of this comes from the problem I was trying to solve in the autonomous vehicle world. This is work that I've been doing for years in DOD aerospace with risk management and cybersecurity and safety, and just running complex programs. It's so much about the paperwork and how you make decisions, how you justify those decisions, how you comply with regulations, understanding the regulations. And for autonomous vehicles the scope was just unprecedented when you look at the number of things that could go wrong. And we could literally talk for the next few days, just me rattling off scenarios, and you'll go like, "Wow, I never thought of that. I never thought of that. Wow, wow." And you just start going like, "How do you manage this?" And well, that was what I was sought out. That's what I was having to solve. And looking at all the different ways of doing this and trying to combine a Bayesian approach with risk management and realizing the data sets were going to be huge and how do you manage that. Daniel: And it kind of turned out to be an unsolvable problem at the time. And around that time, because I was working at Lyft, I got brought up to manage a lot of the issues that were going on with the Lyft actual IPO, which again, more regulatory stuff with the SEC and how processes are applied across the entire enterprise, how these comply with SEC regulations and expectations and how this was audited. And just even how we were measuring our cybersecurity performance as a company, how that was getting reported to the board. Again, very similar problem, just slightly different problem space, slightly smaller scope. And around that time Mark's company, Trust Networks, was actually acquired by Lyft, and I met him and I got introduced more to graphs and knowledge graphs. I actually hadn't even worked with knowledge graphs prior to that. I was much more in deterministic structures and DOD aerospace. Daniel: I was the one always saying, "Why are we writing in this Python? We should write it all in Ada." And all the people would just look at me and go, "What is Ada?" And I would do that just as a joke, but also partially believing it. I still advocate Ada. I like Ada, even if it makes developers cry. It was designed to make developers cry, because it always works, but that's another story. And that was back in what, 2018, 2019?...
April 20 · 29 min
Veronika Heimsbakk: Connecting Data Engineering and Knowledge Architecture – Episode 48
Veronika Heimsbakk With interest in knowledge graphs growing by the day, Veronika Heimsbakk is busier than ever with her efforts to connect the data engineering, information architecture, and ontology practices that drive modern knowledge engineering. Best known as an advanced knowledge graph practitioner and a leading expert on the SHACL standard, Veronika also regularly shares her knowledge through her writing, university courses, and professional workshops. We talked about: her work at Data Treehouse, creating tooling for data people to get on board the knowledge graph journey how she helps data engineers find their overlap with knowledge engineering her work to build bridges between data engineers, information architects, and ontologists how she meets data engineers on their own turf by using simple Python scripts to put their data frames into a knowledge graph how public sector compliance requirements drive demand for RDF solutions the powerful tool that helps her communicate with a variety of stakeholders and collaborators: coloring pencils how she works with information architects and enterprise architects her take on graph visualizations, that they're rarely very useful in helping her communicate with engineers and business people her approach to balance top-down ontological approaches and bottom up data engineering approaches in knowledge graph construction her early work with SHACL and her appreciation for its applicability to a wide range of use cases beyond simple data validation her take on the ongoing OWL versus SHACL discussion her preferred tool for turning modeling sketches into RDF code: WebProtégé how her work with the Norwegian maritime authorities reduced caseworker time on regulatory tasks from several weeks to a few seconds her upcoming masterclass at the Knowledge Graph Conference on transitioning from data engineering to knowledge engineering Veronika's bio Veronika Heimsbakk is a knowledge graph specialist at Data Treehouse with over a decade of experience in semantic knowledge graph technologies. Throughout her career as a consultant, she has served as a developer, architect, advisor, and team lead, working with public and private sector clients across Europe, with a strong focus on the public sector in recent years. Veronika is the author of SHACL for the Practitioner (2025). She is a regular guest lecturer on SHACL at the University of Oslo and has delivered the SHACL Masterclass at various venues for several years. In 2024, she was recognised as one of Norway's Top 50 Women in Tech. On Substack, Veronika writes From Data Engineering to Knowledge Engineering, a practical article series that shows data engineers how to build knowledge graphs using familiar tools like Python, Polars, and maplib, covering everything from ontologies and SPARQL to SHACL validation and reasoning. An eager advocate for logic and linked data, she champions knowledge graphs in a landscape increasingly dominated by predictive approaches. Connect with Veronika online LinkedIn Substack SHACL for the Practitioner book e-mail: sh at veronahe dot no Video Here’s the video version of our conversation: https://youtu.be/cY8rhPoXepE Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 48. Ontology design and knowledge graph building are truly team sports, requiring collaboration across a variety of business and engineering disciplines. Few practitioners are as experienced at bringing these teams together as Veronika Heimsbakk. As both a consultant and as an author and educator, she helps business and public sector stakeholders, data engineers, and knowledge architects understand each other's languages and appreciate each other's practices. Interview transcript Larry: Hi everyone. Welcome to episode number 48 of the Knowledge Graph Insights podcast. I am extremely delighted today to welcome to the show Veronika Heimsbakk. If you've ever been to the Knowledge Graph Conference, Veronika's just, you know her already. She's just the most engaging presence there. She's always got her Norwegian KitKat bars and her Polaroid camera and doing awesome workshops on SHACL and other things. But welcome to the show, Veronika. Tell folks a little bit more about what you're up to these days. Veronika: Thank you, Larry, and thank you for having me. Yes, these days I'm up to in using familiar tooling to get started with knowledge graphs and harvesting all the knowledge graph capabilities and graph traversals as opposed to JOINs and tabular things. Yeah. Larry: Well, this feels like a year in which a lot of that might be happening. A lot of data engineers, there just seems to be so much excitement and interest in knowledge graphs and ontologies. And it's so important to meet people where they are on their journey into that. And you know, you're involved with, I know the data folks in Helsinki and we didn't talk about your background. You're currently a knowledge graph specialist at the Data Treehouse. And previously, you've done consulting like at Capgemini. So you've done a lot of this work hands-on. You wrote a book about SHACL, and you do workshops and a lot of teaching. And part of that whole mindset of yours is currently, maybe not... I guess it's focused on helping data engineers become knowledge engineers. Is that an accurate way of putting it? Veronika: Or at least not fully transitioning maybe from data engineering to knowledge engineering, but finding that intersection of a skillset that's truly powerful in working with ontologies because we have seen the rapid interest and popularity of ontologies lately when large language models took the world by storm. But I've also experienced during my years as a consultant that the ontology things and the knowledge graph aspects, they are usually a concern of the information architects and those who work with concepts and terms and setting them into context and everything. But the information architecture departments usually don't talk to the people working on the data and making applications. So why should we create ontologies that are machine-readable in semantic models? They are a database schema in itself. They are fully usable by data people, but there is something in between there that's hard to grasp. Veronika: So I want to build this bridge because when I was finished at the uni, I started as a Java developer on Symantec Tech project. So I've been doing a little bit of data engineering myself in the early days going from tabular data to RDF and knowledge graphs. But I see that this isn't something that should be separated, of course, if you want to be data-driven, ontology-driven in your applications, you need the data people on board if you're going... Successful project. Larry: Yeah, that's really interesting too, because it seems like there's at least a couple of things there. Just the common language between information architects, data engineers, and knowledge engineers, but then also, in any communication project, meeting them on their own ground. And that probably applies both in the human natural language that you're talking to people about, but also in the technology to implement stuff. And I know that's what you're doing in your day job now, but can you talk a little bit about how you're making knowledge graphs and knowledge engineering more accessible to data engineers? Veronika: Yes, of course. The company that I work for, we create a framework for doing exactly that, like working with knowledge graphs using data frames. So I've been working a lot with that lately and writing a lot of articles on the topic and how you can transition from a tabular data format to queryable knowledge graph, doing graph traversals and answering questions you even didn't know you had, right? But the way that I work is usually together with clients, is applying simple tooling on their tabular data. And these days, most people work in data frames, right. So going from a Polars data frame to queryable knowledge graphs only require three, four lines of Python code by using, for example, maplib, which is a Python framework for handling knowledge graphs as data frames. And you can even get your SPARQL query answers back as a data frame to push further in your data pipeline. Veronika: So you have all these capabilities of graph traversal in answering questions, but also, in inference and enrichment and automating enrichment of completing metadata, for example, and doing validation with SHACL, for example. You have all these knowledge graph capabilities that you can put on top of your existing data infrastructure. Larry: Are there classic use cases where... Is there higher demand in some industry verticals for this kind of thing? Veronika: Recently, in Norway at least, I've seen a rapid demand for like, "Hey, I have all my data in this data lake," like Databricks or Snowflake or whatever. But the information architecture folks, they're building ontologies or they want to reuse the national standards. Like in Norway, we have a set of national standards that are expressed in RDF. It's SKOS for concepts and terms. It's DCAT for data catalogs and it's CPSV for core public services and to be able to describe them. And it's a demand for the public sector to comply to those. And when they have data in Databricks, for example, how can we connect to these national standards or to our internal ontologies with the data in Databricks to make the ontologies operational? Veronika: So that's a use case that I stumble across a lot lately. And I've actually written about this recently because I did a teeny tiny project on that at the Culture Heritage Directorate in Norway. And that again, it's like four lines of Python inside Databricks and you have your ontology operational on your data. Larry: Interesting....
April 6 · 34 min
Joe Reis: Fighting “Context” and Other Tech-Industry Hype – Episode 47
Joe Reis When Gartner declared 2026 "The Year of Context," Joe Reis leapt into action, immediately writing a good-natured satirical article about "context products," "context lakes," and the "analyst singularity." It's a fun article that exemplifies Joe's no-nonsense approach to industry education and concludes with a serious point — "context does matter, and most organizations are terrible at it." We talked about: his forthcoming data modeling book, "Mixed Model Arts" the origins his satirical post "Gartner declares 2026 the year of context" our speculation on how the word "context" came to the fore how his decades of experience help him fine-tune his hype detectors "the one equals 10 dilemma" via which leaders extrapolate AI benefits that senior programmers gain onto less-skilled engineers the challenges that executives miss of building a semantic layer the endless quest for "silver bullets" over solving fundamental business problems the relevance of Einstein's definition of stupidity in the AI hype cycle how the big AI providers are like the ISPs of the 1990s how generative AI has accelerated and improved his workflows the trepidation around AI that he feels when he visits Silicon Valley and San Francisco the unprecedented pace and scale and context of the current AI hype cycle the role of the knowledge community in the current tech environment Joe's bio Joe Reis, a "recovering data scientist" with 20 years in the data industry, is the co-author of the best-selling O'Reilly book, "Fundamentals of Data Engineering." He’s also the instructor for the wildly popular Data Engineering Professional Certificate on Coursera, in partnership with DeepLearning.ai and AWS. Joe’s extensive experience encompasses data engineering, data architecture, machine learning, and more. He regularly keynotes major data conferences globally, advises and invests in innovative data product companies, writes at Practical Data Modeling and his personal blog and hosts the popular data podcast "The Joe Reis Show." In his free time, Joe is dedicated to writing new books and articles and thinking of ways to advance the data industry. Connect with Joe online JoeReis.xyz Joe's writing and podcast Gartner Declares 2026 The Year of Context™: Everything You Know Is Now a Context Product Fundamentals of Data Engineering (O'Reilly), Joe's bestselling book Practical Data Modeling Personal Blog The Joe Reis Show Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=6A_FWL0hbKM Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 47. When Gartner recently declared 2026 "The Year of Context," the gauges on Joe Reis' industry hype dashboard maxed out. Joe's a respected veteran of the data profession, known for his best-selling book, Fundamentals of Data Engineering, and for his courses, newsletters, conference keynotes — and especially for his no-nonsense takes on industry trends. He's also a good friend of the knowledge graph community. "Context" is just his latest tech-industry hype take-down. Interview transcript Larry: Hi, everyone. Welcome to episode number 47 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Joe Reis. Joe is a well-known figure in the data engineering and data world. He's the co-author of the book, Fundamentals of Data Engineering, which is kind of a category-setting book. He's working on a new book called Mixed Model Arts, on data modeling, and does a lot of other interesting stuff. He's really well known in the conference community. And anyhow, welcome to the show, Joe. Tell the folks a little bit more about what you're up to these days. Joe: Hey, what's up, Larry? What have I been up to lately? Just been editing Mixed Model Arts. I just actually finished, I guess, the main edits and just down to the very minor tweaks as of today. So that's awesome. So literally just working on that before we hopped on and I'll be working on that after we're done. Larry: Okay, Great. Well, sorry to interrupt your book. I'm a former book editor, so I always feel bad when I interrupt progress like that. Congrats. Joe: It's okay. Thank you. Larry: Do you have a publisher for the book? Joe: That would be yours truly, yes. Larry: All right. Okay. Well, anyhow, we'll keep the webpage- Joe: We'll talk about that later. Yep. Larry: Yeah, with info about where to get it. Well, hey, the reason this conversation came together, there was this great little convergence of meeting of ideas a couple of weeks ago. I had just done a presentation where I was talking about how hyped the AI cycle is. And then in quick succession, I saw a post from Juan Sequeda where he talked about some folks have mixed feelings about Gartner. And then I came across this post you had done, "Gartner declares 2026, the year of context." It was this brilliant satirical piece. Can you talk a little bit about that and what motivated it and just maybe a quick outline for folks? Joe: Yeah, I mean, I think spawned from... I guess my social media circles were like Gartner, and all of a sudden I started seeing my LinkedIn feed bombarded with the word context and how Gartner declares this the year of context and... I can swear in your show, right? Larry: Yeah. Joe: Okay, shit. Larry: It's fairly family friendly, but yeah. Joe: Yeah, it's all good. So I've seen them and similar research firms in the past declare this, that, or the other thing. And I just felt like this in particular seemed... And no offense to the knowledge graph folks there, whatever, you're all great. And I think it serves knowledge graph community really well, but the year of context I think is jumping in the gun a bit too fast. Where last year was a year of agents, year before that was year of AI or whatever, and it just seems like... It's what I described as the buzzword industrial complex where we jump... Not we, but certain groups in the industry need something new to push onto people in order to keep, I guess, discussions going, in order to keep people attending conferences, in order to keep selling consulting services and all this other stuff. Joe: And so I felt like this was really just another instance of it, but I decided that I had had a few spare cycles in between editing my book. So I was like, "Oh, let's just write a satirical piece on this," maybe somewhat satirical, maybe just kind of poking fun at just, I guess, the nonsense of the industry that we keep finding ourselves in over and over again. So that was all there was to it, Larry. Larry: Okay. Well, one of the ways you contextualize that was this, I forget what you call it, the conference content capital cycle, this self-reinforcing loop, which appeared to me to mirror this kind of whatever that bizarre financial loop that's keeping the AI companies up. Was that intentional or was I just reading into that? Joe: I mean, I don't know if it was intentional, but it's just an observation that I've noticed in that article, and I think a few others, where it was very much... It's a self-sustaining thing where you need the news story, you need this. And it's the same as the AI hype cycle right now where it's just a very circular system. And so just that the money just sort of rotates around and that's just kind of how it is amongst strangely a lot of the same players, which I think is kind of funny. Larry: Interesting. Yeah, so maybe we've just stumbled upon some universal dynamic that drives various kinds of hype cycles. But one thing that occurred to me is there's always some fundamental underlying, it's business anxiety or truth or something like that that's driving these things. The context thing, do you have any hunch where that came from? I remember it just hit my LinkedIn feed, what, three or four months ago and it's been constant ever since. Joe: I'll ask you this actually. I mean, let me reverse the roles of a host and guest here. I mean, you've been in the knowledge space for a while and I imagine that some manifestation of the word context has come up in your discussions with your peers. So I guess if I'm in your shoes and those of your peers, what's it like to see a word like context or semantics or ontology or graphs becoming these sort of terms du jour? Larry: Well, in one sense, it's really gratifying, of course, because we're on the radar screen. You can actually say ontology in public now, which has not been the case for the last 10 years. Joe: Yeah, you get jailed for doing that. Yeah. Larry: Exactly, yeah. Put you in the stocks in the middle of the courtyard. But no, so it's really interesting. And that's one of the reasons I'm curious about your take on it, because it's like there's these real things that drive it. But in terms specifically of context, I was just reminded just of... Somebody on LinkedIn today just shared a post I did recently about Dave McComb's... I don't want to get too nerdy, but this is a Knowledge Graph Insights podcast, so I'll set a little context. There's this thing in knowledge graph construction. You have the A box, the assertion box, which is like all the things, all the data instances that are in there. Then you have above that, you have the T box, which is the concepts that describe it, the ontology basically, typically. Dave McComb, who I think you must know, because the data centric enterprise and all that. Joe: Mm-hmm. Larry: He articulated this notion, I don't know, a couple of years ago of the CBox. And what was really interesting in this post I saw today is that he used it as the categorization box. That's where you put all the taxonomic terms, vocabularies, all that sort of what I think of as the metadata about the data is sort of in there. And I didn't realize at the time,...
March 16 · 37 min
Robert Sanderson: Building Yale’s Cultural Heritage Knowledge Graph – Episode 46
Robert Sanderson Yale University manages huge collections of precious cultural heritage artifacts housed in multiple museums, libraries, and other collections. Using knowledge graph and ontology engineering design patterns that he has developed over his career, Robert Sanderson helps scholars, researchers, and the general public access information about — and make connections across — millions of unique items in Yale's collections We talked about: his work as Senior Director for Digital Cultural Heritage at Yale University the knowledge graph and ontology engineering design patterns that guide his work the scope of his work — improving discoverability of Yale's extensive collections of artifacts, facilitating the management of collection information, and even collecting data on physical artifact storage facilities how their linked data approach lets researchers easily connect information about artifacts and information housed in multiple museums, libraries, and collections how the growth of LLMs has affected their KG user interfaces how AI is accelerating their ability to add to their knowledge graph the millions of artifacts in their collections that aren't yet accounted for the compact nature of their three-billion-triple KG ontology, just 10 classes and 50 relationships the extensive vocabularies and taxonomies they use how they handle the need to reconcile the identity of lesser-known people who don't have a Wikipedia page or other authoritative references available how they balance the competing needs of comprehensiveness and usability as they build their knowledge graph how knowledge graphs facilitate discoveries that other search tools can't current opportunities for post-docs to join his team to work on leading-edge AI projects Robert's bio Dr. Robert Sanderson is the Senior Director for Digital Cultural Heritage at Yale University, where he works with the libraries, archives, and museums to ensure that data and other digital efforts are coherent and connected. He is the principal architect for Yale’s cross-collection discovery system, LUX, which is built on the Linked Art specifications, for which he is an editor. He is also an editor for the IIIF specifications, was the co-chair and editor for JSON-LD and the Web Annotation data model in the W3C. He has previously worked at the Getty in Los Angeles, Stanford University, Los Alamos National Laboratory, and the University of Liverpool. His current areas of work and research are at the intersections of cultural heritage, knowledge graphs, data usability, and generative AI. Connect with Rob online LinkedIn email: robert dot sanderson at yale dot edu Rob's LinkedIn post series on KG and ontology design patterns The 10 Design Principles to Live By Ontology Design Patterns Naming Things Avoiding Reification Foundational Ontologies Multiple Inheritance, Not Multiple Instantiation Predicate Reuse... Meh Document your ABCs Separate Query and Description Semantics Usable vs Complete acknowledgements Video Here’s the video version of our conversation: https://youtu.be/SMAVyrL3aSU Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 46. When your job is to help scholars and the public discover information about millions of cultural heritage artifacts that are housed in multiple museums, libraries, and other collections, you need a powerful — but also manageable — knowledge graph. That's Rob Sanderson's role at Yale University. He and his team apply time-tested ontology and knowledge engineering design patterns to help people discover — and see the connections between — these precious human artifacts. Interview transcript Larry: Hi everyone. Welcome to episode number 46 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Robert Sanderson. Rob is a professor and the senior director of Digital Cultural Heritage at Yale University, the Ivy League School in Connecticut. Welcome to the show, Rob. Tell the folks a little bit more about what you're up to these days. Rob: Hi, Larry. Thank you so much for inviting me to be part of the illustrious lineup of guests on your podcast. So yeah, I'm Rob Sanderson, as you said, Senior Director for Digital Cultural Heritage at Yale. So I work with the libraries, the archives, and the museums and other collecting organizations at Yale to help them to be more connected with linked data organizationally and more coherent in the way that we do things digitally. So our projects really focus on discovery and access to the collections in service of the university mission, which of course is teaching and learning, research, and preparing our students to be the next generation of leaders in the world. Rob: So for that, the university invests very heavily in the collections, which is fantastic. We are super proud of the 300 years of collecting that we've done. But we want to make sure that if you can't come to New Haven, you still have as good access to those collections as possible. And the ability to find amongst the many millions of objects that we steward exactly what it is that you need. So a lot of our projects focus on describing the collections in a more computationally tractable way so that that discovery can be better. And also how to manage the information that's associated with the collection, but isn't a museum object or a archival object itself. For example, I have two postdocs that are openly available. So if you are a few years out of your PhD or just about to graduate, do get in touch to work on how to use AI to extract the ownership history or the provenance of particular museum objects from the archival content that we also manage. Equally, how can we align research data sets with the collections? So we also have a natural history museum as well as two art museums. How can we align the environmental datasets that are out there on the web with the natural history specimens that could have been impacted by those environments? Rob: Yeah. And then equally, we look at the environment of Yale. So we have a large project at the moment to set up environmental monitoring with sensors for light, for humidity, temperature, and so on, to be able to generate a large data warehouse aligned with linked data with the collections so that we can have evidence of what the effects of the environment are on the collection items themselves. Larry: Interesting. That is so fascinating. What a fascinating remit. One quick thing about what you just said. Is that about humidity and temperature and all the things that might affect the endurance of these physical artifacts? Rob: Yep. Yes. That's right. Larry: Yeah. Rob: We have about 200 sensors around the place monitoring every five minutes a new data point, which if you think about it, it's actually not that much data. Larry: Yeah. I have to say, I just love that you're doing data stuff along with it. That you're not just sitting in a dusty old room collecting things. You're doing cool modern stuff too. But hey, I want to quickly interject how we met, and I just want to put this in because we won't have time to talk about it today, but I want people to know about this fantastic series you did. That's how we met was somebody drew to my attention the series you've done on ontology design and on knowledge engineering design patterns. And I'll point to that in the show notes, but I just wanted to mention. And the more I think about what you just said, because I didn't know all of this background before we started recording, I'm like, "Oh, this is even better than I thought." So I'll point to that in the show notes. Larry: But the main thing I wanted to talk about today is what you were just talking about. This amazing cultural heritage operation that you're running there, especially the knowledge graph component of it and the AI, of course, because we're in the 21st century, and that's all anybody talks about. One of the things we talked about before we went on the air was how AI is accelerating the ability for you to build your knowledge graphs of these cultural heritage artifacts and data. Can you talk a little bit about that, how AI is helping in that? Rob: Yeah. Of course. Absolutely. So just a little bit of a background about the knowledge graph itself first before I get to the AI part. So over the past five years, we've built without AI, a very large scale knowledge graph, well, in cultural heritage terms of very large scale, which has about three billion triples in it. And it follows the principles and the design patterns that you mentioned in those posts on Linked Art. It then aligns the people, places, concepts, events, objects, works, collections that we manage here at Yale across the two art museums, Natural History Museum, the dozen or so libraries. There's also a collection of musical instruments, the Institute for the Preservation of Cultural Heritage, and we even have a little outpost in London, in England for art history research that we include. So that work uses the linked art ontology, which is based on the foundational site CRM ontology and is publicly available both in terms of the data, you can just download it. But also in terms of the graph queries, we don't force you to learn SPARQL. We have a user interface on top of it, which allows you to generate queries and find the objects that you are looking for. Rob: So one of the things that we noticed first about the user interface is that only about 5% of searches are actually using the graph affordances. Mostly, 95% of the time, people just put in keywords because that's what they're used to. You go to Google, you type in your five favorite keywords that you think might match and you scroll through the results. However, now in 2026, people are more used to typing in full sentences and then having AI take t
March 2 · 35 min
Max Gärber: Agentic AI Built on a Knowledge Graph Foundation – Episode 45
Max Gärber The promise of agentic AI is being realized in systems like the Service Copilot that Zeiss microscopes provides for its field service engineers. The system integrates technical documentation, subject matter expertise, and user-generated insights which are orchestrated and shared with a suite of AI agents. While it relies heavily on modern LLM technology, it's the system's solid knowledge graph and metadata foundation that make it a success. We talked about: Max's work "turning information into value" at PANTOPIX, a technical documentation and information processes consultancy based in Germany a recent client project working with Zeiss to help their field service engineers operate more efficiently how their prior knowledge management and machine learning work helped them not only cope, but thrive, at the arrival of ChatGPT and LLMs the immediate positive stakeholder feedback they received as they incorporated LLMs into their knowledge architecture how they extended the iiRDS standard with a custom ontology and taxonomies and integrated topic mappings into their system and workflows an overview of the system architecture and tooling, which includes both a graph database and a vector store, an ontology and taxonomy management tool, and documentation of best practices their evolution from simple prompt engineering and RAG approach to an agentic orchestration architecture a few of the agents in their architecture: a planning agent that organizes and orchestrates a content agent that replaces the original RAG system a troubleshooting agent which surfaces past solutions the good problem they experienced of managing enthusiastic user adoption of the new system the unexpected benefits to the Zeiss sales team of the system how subject matter expertise, user generated content, and other insights are captured and used the crucial role of knowledge management practices, structured content, and semantic technology in building the foundation for an organization's AI capabilities Max's bio Maximilian Gärber is Partner and Principal Technical Consultant at PANTOPIX. Max has been working in the field of technical communication for over 15 years. As a Partner and Technical Consultant at PANTOPIX, he is responsible for the technical consultation and implementation of projects. In addition to project management, Max is responsible for data modelling and process optimization in relation to product information (migration, publication, translation) and product catalogues. He is also responsible for product development and ensures that innovative solutions for our customers are continuously developed and optimized. Connect with Max online LinkedIn PANTOPIX Resources mentioned in this episode Industrial Knowledge Graph meets Agentic AI: Service Copilot at ZEISS RMS slide deck Service Copilot from ZEISS article Video Here’s the video version of our conversation: https://www.youtube.com/embed/ttQOHvvxPyw Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 45. When you're a field service engineer dealing with both the typical challenges of information overload and the need to maintain complex machinery like a high-end Zeiss microscope, you'd really benefit from an intelligent knowledge management system, one that integrates technical documentation, subject matter expertise, and user-generated insights. That's exactly what Max Gärber has built - an agentic AI system grounded in a solid knowledge graph foundation. Interview transcript Larry: Hi everyone. Welcome to episode number 45 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Max Garber. Max did a really interesting presentation at the Semantics conference in Vienna last fall, and I've been trying to get him on the show ever since. So here he is. I'm excited to have him here. Max, he's a partner and a technical consultant at PANTOPIX, a consultancy based here in Germany. Welcome, Max. Tell the folks a little bit more about what you're doing these days. Max: Yeah, thanks Larry. Thanks for having me. Yeah, great show. And yeah, we are mostly concerned with helping mainly our industrial customers structure their content and integrate it from various sources into their systems, delivery systems, wherever it is needed. So yeah, it's mainly consultancy on data modeling, on how to do information processes and how to get the best out of your data, so to say. So our mission here is literally turning information into value. Larry: Oh, I love that. That's a great tagline for a consultancy. Well, you did the use case, the case study you talked about in Vienna was really interesting to me. This issue of Zeiss microscopes, in particular their research microscopy solutions arm, which is these big, expensive, complex machines that require a lot of service. Can you talk a little bit about how you got involved with Zeiss and what you do to help them? In particular, the thing you talked about in Vienna was about the system to help their field service engineers. Can you talk a little bit about that project? Max: Yeah, exactly. The main objective there was helping the field service engineer to get the information in that situation when they need it and in the format they need it. That is essentially the bottom line of it. And it started essentially as a knowledge management project. Zeiss, RMS, they have been really into structuring, getting structured content, adding proper metadata to it so it can be used in various cases. The idea has been to integrate from various sources, spare part system, for example, or the manuals from the technical documentation or ticket information and get them into one system so there's a single point of access for the service technicians. So they don't need to spend a lot of time in all of the different systems that there are to get the information about that case they are currently working on because there's a lot they need to consider when servicing or troubleshooting a microscope. Max: And yeah, that project evolved into what is now the Service Copilot because I think it was in early '22 when we started the project. And one part of it was to not only integrate all of that information in one place, but also recommend content to the service technician. So, if you were working on a specific case, so the ticket was known, the product was known, you should get a recommendation of articles, "Hey, this is how you install this and that component," for example. So we actually worked a lot on labeling tickets. We actually had a custom labeling interface and used, let's say, classical machine learning approaches to get that recommendations done. Max: And it worked not so good, but that was also the same time when GPT, I think it was 3.0 or 3.5 came out. And yeah, we were faced with that situation that there was a new technology available that looked like it could do everything and much more what we were currently doing without much effort. So we really faced the situation there to either stop the project or reinvent ourselves, I would say. Larry: I love that juncture. We were talking a little bit before we went on the air about you were really concerned at that point as this arose, but then it turns out that the prior work you had done, the knowledge management work you had done and the machine learning skills and workflows and things you developed, it turns out you ended up being, to my mind, it looks like from that demo I saw in Vienna, at the leading edge of hybrid AI architectures and agentic AI. Max: Yeah, I mean, totally. It evolved really quickly. At the point where we looked into GPT and what language models could do, we asked for, "Hey, can we do some quick prototyping research on this and see if we can replace, let's say, the machine learning pipeline that we had with language models?" And it worked really well from the start. So in the beginning, we had 15 service technicians as pilot users that were constantly evaluating the system and giving us feedback, "Hey, that's good, that's not good." And they said immediately, "Well, this is working really well." I mean, they tried, of course, at the very beginning to trick the system and ask the hard questions. And if you look at the content that they are provided, a service manual, it has hundreds of pages and the products that they are servicing, they look quite similar, but they are quite different. Max: So there's a lot of variants in what components you can use, how you configure the system, how you buy it. So it's really important that if you have a certain product variant, you don't mix that up. And if you look at how the content is, it is very similar. So of course they have the same structure or a very similar structure and certain, let's say, chapters or topics, they are always very similar. So how you install electron microscope A is very similar to how you install electron microscope B, but it's the little differences that are really important if you are doing that installation procedure. If you forget one of those steps, of course, you will fail or you could even do some harm to the system. So it's really important that you not only have similar content or similarity in, let's say, the retrieval of the content, but you can actually know, "This is content for product A and this is content for product B." Max: So all of the work that went into structuring the content, adding metadata to each of the topics and connecting the metadata based on what entities are linkable, the RAG system that we implemented then, it could actually filter out all of the content that was not relevant to the specific question or use case. So the answers were quite good from the beginning. Larry: Yeah. I want to elaborate a bit on the evolution of your RAG architecture, and for folks who don't......
February 16 · 29 min
Quentin Reul: Solving Business Problems with Neuro-Symbolic AI – Episode 44
Quentin Reul The complementary nature of knowledge graphs and LLMs has become clear, and long-time knowledge engineering professionals like Quentin Reul now routinely combine them in hybrid neuro-symbolic AI systems. While it's tempting to get caught up in the details of rapidly advancing AI technology, Quentin emphasizes the importance of always staying focused on the business problems your systems are solving. We talked about: his extensive background in semantic technologies, dating back to the early 2000s his contribution to the SKOS standard an overview of the strengths and weaknesses of LLMs the importance of entity resolution, especially when working with the general information that LLMs are trained on how LLMs accelerate knowledge graph creation and population his take on the scope of symbolic AI, in which he includes expert systems and rule-based systems his approach to architecting neuro-symbolic systems, which always starts with, and stays focused on, the business problem he's trying to solve his advice to avoid the temptation to start projects with technology, and instead always focus on the problems you're solving the importance of staying abreast of technology developments so that you're always able to craft the most efficient solutions Quentin's bio Dr. Quentin Reul is an AI Strategy & Innovation Executive who bridges the gap between high-level business goals and deep technical implementation. As a Director of AI Strategy & Solutions at expert.ai, he specializes in the convergence of Generative AI, Knowledge Graphs, and Agentic Workflows. His focus is moving companies beyond "PoC Purgatory" into production-grade systems that deliver measurable ROI. Unlike traditional strategists, he remains deeply hands-on, continuously prototyping with emerging AI research to stress-test its real-world impact. He doesn't just advocate for AI; he builds the technical roadmaps that translate the latest lab breakthroughs into safe, scalable, and high-value enterprise solutions. Connect with Quentin online LinkedIn BlueSky YouTube Medium Video Here’s the video version of our conversation: https://youtu.be/J8fgIezoNxE Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 44. We're far enough along now in the development of both generative AI learning models and symbolic AI technology like knowledge graphs to see the strengths and weaknesses of each. Quentin Reul has worked with both technologies, and the technologies that preceded them, for many years. He now builds systems that combine the best of both types of AI to deliver solutions that make it easier for people to discover and explore the knowledge and information that they need. Interview transcript Larry: Hi, everyone. Welcome to episode number 44 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Quentin Reul. Quentin is the director of AI Strategy and solutions at expert.ai in the US in Chicago. So welcome, Quentin. Tell the folks a little bit more about what you're up to these days. Quentin: Hi, thank you, Larry, for accepting me and getting me on your podcast. So my name is Quentin Reul. I actually have been around the RDF and the knowledge graph since before it was cool in the early 2000. And today, what I'm helping people in news, media, and entertainment is to see how they can leverage all of the unstructured data that they have and make it in a way that can be structured and they can make their content more findable and discoverable as part of what they are offering to their customers. Larry: Nice. And I love that you've been doing this forever. And one of the things we talked about before we went on the air was your early involvement in the SKOS standard. Can you talk a little bit about your little contribution to that project? Quentin: Yeah. So for this, we do know what SKOS stands for Simple Knowledge Organization System. It's a standard that has been created by the W3C standard around 2005. And being at the University of Aberdeen in Scotland, we had a lot of involvement with the W3C voicing the web ontology language and SKOS. Quentin: For SKOS, I was actually working on my PhD, and the idea of my PhD was to look at two ontologies and trying to map entities from one ontology to the entities in the other one. And a lot of the approach that were taken at the time were either leveraging philosophical kind of representation. And there was not really a lot of things that were looking at linguistics. So the approach that we were taking was looking at WordNet and using the structure of WordNet and maps that to the linguistic information, so the labels that were associated with nodes in the taxonomy. Quentin: But to do that, we needed to have a structure that was transitive. And at the time, SKOS only had broader and narrower, and they didn't have the transitive property. So my contribution was to push for the W3C standard and SKOS to include the SKOS broaderTransitive and SKOS narrowerTransitive, so that I could now have that if A broader B and B broader C, that A broader C was also correct, and having that description logic structure that would enable that. Larry: Well, that's so cool. I love that you have your ideas are ensconced in this 20-year-old standard now. But hey, what I wanted to talk about today and really focus on, I know I was excited to get you on the show because you're doing a lot of work in the area of neuro-symbolic AI, the idea of integrating LLMs and other machine learning technologies with knowledge graphs and other symbolic AI stuff. Larry: It's one of those things that everybody's talking about, but I haven't had the chance to talk on the podcast with many people who are actually doing it. So I'm hoping that you can help the listeners take the leap from this conceptual understanding of the natural complimentary nature of them to actually putting them together in an enterprise architecture. I guess maybe start with the strengths and weaknesses of each of the kinds of AI that we're talking about here. Quentin: Yeah. So if we look at the history of AI, symbolic AI was a thing that came up in the '70s and led to the first AI winter and the second AI winter for that matter. But where they were very good was in the structure and the explainability. So if you aren't very well set set of rules or predictive kind of aspect, it would do it consistently, repeatably, and all of that type of things. Quentin: Now, when you were trying to adopt a rule-based system for new data, it would die off because you had never seen that or a new set of rules or a new set of business requirements, it would just not handle that. And that's where machine learning really helped in making that transition to where we are today. Quentin: And the LLM, contributing further to that, in as much as the machine learning was pretty good at dealing with new patterns, as long as it was similar to the data that you were training with. I think one thing that the LLMs have really shine is in the way that it's able to surface things that you were not predicting from the data. Quentin: One thing that I think that we could have predicted or seen from the data if we had LLMs back in 2020 is we could probably have seen the topic of COVID emerging a bit earlier than what it did. And the reason is, it's because it's very good at surfacing things that it's never seen before. It's able at interpreting the language and analyzing the language in its structure. And by the sentence structure, understanding that things are very similar, and you may use different words for them, but now you're able to interpret them. Quentin: So if we think about information retrieval in the '90s, 2000s, and even in the 2010s, the way that we were doing a lot of these things was using control vocabulary, CISORI, or other dictionaries, and they were used to do query expansion. So you add a keyword, you were looking in the dictionaries, the dictionary were doing an expansion, and then you add something else. Quentin: Well, now with the LLM, that kind of expansion is intuitive to the actual LLM because you had seen so many different aspect and so many occurrence of text that it can actually predict and see what these different terms are associated with a holistic concept. Quentin: Now, that's a good thing. On the bad thing, the LLMs don't have ... Well, they have a cutoff point or knowledge cutoff point, which means that when they are trained, they are trained of information that is in the past. So they're not always that great at predicting, especially current event or information about things that are happening today, they're not very good at that. Quentin: I think if I look at the data, generally between the release of a new model and the nature of the data or the cutoff point, it's about six months to a year. This is like going a bit slower now or shorter in terms, but you have to remember that the time that it takes to train these models, we're speaking about days, weeks, and sometime months as opposed to hours with machine learning models. So they're expensive as well from that perspective. Quentin: Another aspect that they don't have, it's a knowledge base to just take a higher level from a knowledge graph, like the knowledge base. So it's not able to disambiguate information in a large corpus. It's very good to do entity linking within the context of one document. Quentin: So if you pass it one document, let's say a financial document, and it refers to Acme as an enterprise, if Acme is mentioned several times during the document, it will infer that there is only one entity and that entity is Acme. Quentin: But now, imagine that you have a group of financial reports, and these financial reports refer to Acme, a bakery in Illinois, and Acme, a construction company in Maryland....
January 22 · 54 min
Jim Hendler: Scaling AI and Knowledge with the Semantic Web – Episode 43
Jim Hendler As the World Wide Web emerged in the late 1990s, AI experts like Jim Hendler spotted an opportunity to imbue in the new medium, in a scale-able way, knowledge about the information on the web along with its simple representation as content. With his colleagues Tim Berners-Lee, the inventor of the web, and Ora Lasilla, an early expert on AI agents, Jim set out their vision in the famous "Semantic Web" article for the May 2001 issue of Scientific American magazine. Since then, semantic web implementations have blossomed, deployed in virtually every large enterprise on the planet and adding meaning to the web by appearing in the majority of pages on the internet. We talked about: his academic and administrative history at the University of Maryland, Rensselaer Polytechnic Institute, and DARPA the origins of his assertion that "a little semantics goes a long way" his early thinking on the role of memory in AI and its connections to knowledge representation and to SHOE, the first semantic web language his goal to scale up knowledge representation in his work as a grant administrator at DARPA how different departments in the US Air Force used different language to describe airplanes the origins and development of his relationship with Tim Berners-Lee and how his use of URLs in SHOE caused it to click how he and Berners-Lee brought Ora Lassila into the semantic web article how his and Berners-Lee's shared interest in scale contributed to the "a little semantics goes a long way" idea why he lives in awe of Tim Berners-Lee Berners-Lee's insight that a scaleable web needed the 404 error code how including an inverse functionality property like in a relational database would have ruined the semantic web how they came to open the Scientific American paper with an anecdote about agents his early involvement in the AI agent community along with Ora Lassila their shared conviction of the foundational importance of interoperability in their conception of the semantic web how the lack of interoperability between big internet players now is part of the reason for the inability to fully execute on the agent version they set out in the SciAm article the impact of LLMs on the semantic web early examples of semantic web linked data interoperability Google's reclamation of the term "knowledge graph" the reason that the shape of the semantic web was always in their mind a graph how the growth of enterprise data led to their adoption of semantic web technology how the answer to so many modern AI questions is, "knowledge" Jim's bio James Hendler is the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI where he also serves as a special academic advisor to the Provost and the Head of the Cognitive Science Department. He also serves as a member of the Board, and former chair of the UK’s charitable Web Science Trust. Hendler is a long-time researcher in the widespread use of experimental AI techniques including semantics on the Web, scientific data integration, and data policy in government. One of the originators of the Semantic Web, he has authored over 500 books, technical papers, and articles in the areas of Open Data, the Semantic Web, AI, and data policy and governance. He is the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. In 2010, Hendler was selected as an “Internet Web Expert” by the US government, helping in the development and launch of the US data.gov open data website and from 2015 to 2024 served as an advisor to DHS and DoE board. From 2021-2024 he served as chair of the ACM’s global Technology Policy Council. Hendler is a Fellow of the AAAI, AAIA, AAAS, ACM, BCS, IEEE and the US National Academy of Public Administration. In 2025, Hendler was awarded the Feigenbaum Prize by the Association for the Advancement of Artificial Intelligence, recognizing a “sustained record of high-impact seminal contributions to experimental AI research.” Connect with Jim online RPI faculty page People and resources mentioned in this interview Tim Berners-Lee Ora Lassila Deb McGuinness The Semantic Web, Scientific American, May 2001 Introducing the Knowledge Graph: things, not strings Massively Parallel Artificial Intelligence paper Attention Is all You Need paper Vision conference Is There An Agent in Your Future? article "And then a miracle occurs" cartoon Jim's SHOE (simple HTML ontology extensions) t-shirt Video Here’s the video version of our conversation: https://youtu.be/DpQki6Y0zx0 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 43. Twenty-five years ago, as AI experts like Jim Hendler navigated the new World Wide Web, they saw an opportunity to imbue in the medium, in a scale-able way, more knowledge than was included in the text on web pages. Jim combined forces with the web's inventor, Tim Berners-Lee, and their mutual friend Ora Lasilla, an expert on AI agents, to set out their vision in the now-famous "Semantic Web" article for Scientific American magazine. The rest, as they say, is history. Interview transcript Larry: Hi everyone. Welcome to episode number 43 of the Knowledge Graph Insights Podcast. I am super extra delighted today to welcome to the show, Jim Hendler. Jim, I think it's fair to say he literally needs no introduction. He was one of the co-authors of the original Semantic Web article in Scientific American. He's been a longtime well-known professor at Rensselaer Polytechnic Institute. So welcome, Jim. Tell the folks a little bit more about what you're up to these days. Jim: Sure. Just to go back a little further in history, I've been doing AI a long time and my first paper was about '77, but a lot of the work we're going to be talking today happened when I was a professor at the University of Maryland, which was from '86 to 2007. And then from 2007 on, I've been at RPI where I was really hired to create a lab that really would be a visionary lab on semantic web and related technologies. I think the president of the university saw the data science revolution coming and saw that that was a key part of it. Jim: So who am I? What am I? Really, what happened was very early in the days of AI, I was working in a lot of different things. I started under Roger Schank at Yale, took a few years off to work professionally at Texas Instruments, which had the first industrial AI lab outside of the well-known ones at Xerox Park and stuff. Then decided no, I really was an academic at heart. So I came back, went to grad school with Gene Charniak at Brown and went from there to the University of Maryland. So you know my job life history. I've bumped around during that time. Living in Maryland, you tend to bump into the Defense Department and things like that and funding and things like that. I was on a few committees and things like that. Eventually asked to come to DARPA for a few years, which is really where a lot of our conversation today probably starts. Jim: And then again, just because it was successful and we had a visionary president here at RPI, she asked me to come and said, "Not only do I want to hire you, but I want you to hire a couple other people you'll work with who'll help put us on the map and this stuff." And I hired Deb McGuinness and I'm sure that'll come up later. And then past 15 years have been a combination of research and administration. So I've done both, doing my own work, working with my students, and also trying to really set up some significant presence of AI on our campus, AI and beyond. Larry: Nice. Yeah, and we'll talk definitely more about your research work and everything. But hey, I want to set a little bit of context about how we met, because I know Dean Allemang from the Knowledge Graph Conference community, and we'll talk a little bit more about the book that you wrote with him later on. But one of the things that he famously says, and always attributes it to you, is that phrase "A little semantics goes a long way." I'd love to open up by talking a little bit about that. Jim: So early on in AI, it was becoming very, very clear to me, and now I'm talking 70s, early 80s, so a long time before we were where scaling means what it does today. But it's very clear to me that a lot of the problem with AI is it didn't scale. And meanwhile, I was seeing these other technologies coming along, the ones that really led to the web, that were looking at a much, much broader thing than the typical AI system. So one of the things I started asking is, how do we scale up AI? And we were looking at traditional knowledge representation languages. I actually have a paper from the 80s. I actually did a book with Hiroki Katano, who's now the... I believe he's still the vice president for research at Sony, if not something higher. And Katanosan and I actually had a book called Massively Parallel Artificial Intelligence in the 80s, but it became clear to me that the machines were part of the story, but the lots and lots of people doing lots and lots of different things was the much more interesting part of the story. Jim: And then also, I've always been intrigued by human memory. You asked me a question and I not only answered that question, but I'm doing right now. It's associating a million things in my mind. And what I'm really doing is winnowing rather than trying to come up with the precise answer. And so I started thinking about how does AI memory start to look like human memory more? In those days, a thousand and then 10,000 and then a million "axioms" were very, very large things, and that's what I wanted to do. And then the web was coming along and I saw that, well, if I'm going to get a million facts about something,...
January 12 · 34 min
Brad Bolliger: Pragmatic Semantic Modeling for Government Data – Episode 42
Brad Bolliger Brad Bolliger entered the knowledge graph space via enterprise software system design and data analytics. That background informs their pragmatic and strategic approach to the use of semantic technology in systems that facilitate information exchange across government agencies. We talked about: their work at EY (Ernst & Young) on data and analytics strategy assessments and enterprise software design and as a co-chair of the NIEMOpen Technical Architecture Committee how their work on EY's Unified Justice Platform introduced them to the knowledge graph world a quick overview of entity resolution the NIEM standard, its origin in the wake of 9/11, its scope, how it's built and managed, and how governments use it their pragmatic approach to ontology and vocabulary management the benefits of the extensibility of the RDF format and knowledge graph technology how entity-centric data modeling accelerates and facilitates systems evolution their take on "analytics enablement engineering" their approach to crafting AI-ready data and building AI-aware enterprise solutions some of the neuro-symbolic AI architecture's they have seen and implemented their call for more systems thinking and systems analysis to create more effective services that work together in a more ethical and effective way Brad's bio Bradley Bolliger (they/them) works in the AI & Data practice of Ernst & Young and serves as co-chair of the NIEMOpen Technical Architecture Committee, an OASIS open standards project for data interoperability. Brad assists clients across various industries with optimizing data platform ecosystems, enhancing customer relationships, and leveraging advanced analytics tools and techniques in their digital transformation efforts. In addition to designing data platforms and AI/NLP systems, Brad has served in lead analyst roles for public sector information system modernization efforts, including major contact center data ecosystems and integrated criminal justice system environments, the latter of which would lead to the development of the UnifiedJusticePlatform. Connect with Brad online LinkedIn Unified Justice Platform Video Here’s the video version of our conversation: https://youtu.be/8XCmF3qXv1E Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 42. When you have to account for the people and other entities involved in high-stakes situations, you need a system that delivers accurate, unambiguous information. Brad Bolliger does this in their work on EY's Unified Justice Platform. Brad is relatively new to the graph world and has adopted a pragmatic approach to semantic modeling and knowledge graphs, focusing on applying lessons learned in their extensive experience in enterprise systems design and data analytics. Interview transcript Larry: Hi, everyone. Welcome to episode number 42 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Brad Bolliger. Brad works in the AI and data practice at EY, the big consultancy in Chicago, and also helps co-chair the NIEM Information Exchange, the Info Exchange Network and standard. Welcome, Brad. Tell the folks a little bit more about what you're up to these days. Brad: Thanks for having me, Larry. I'm thrilled to be talking to you today. Yeah, I'm non-binary. I use they/them pronouns, and I work in the AI and data practice at Ernst & Young, as you said, where I do data and analytics strategy assessments and enterprise software design, things like that. I'm also co-chair of the NIEMOpen Technical Architecture Committee, which is an Oasis Open standard for sharing data in public services primarily, but for specification for developing information exchanges. And I'm working on semantics and software design more generally. Larry: Yeah. And you kind of not stumbled, but you had semantics thrust upon you in this new role, I understand, 'cause one of the projects you work on, I don't know if you're still working on it, was the Unified Justice Platform at EY. Can you talk a little bit about that and how it brought you into the semantics world? Brad: Yeah, that's right. It spun out of an assessment from a county government wanting to overhaul their integrated justice system, which was the collection of actors who collaborate or have this adversarial relationship to administer the process of justice in their jurisdiction. And because very often they're their own elected officials with their own budgets, they have their own software to fulfill their own functions. And that means that they are kind of inherently operating a distributed system, sending messages back and forth to say, "Hey, we booked this person into the jail. Hey, we've got this court date coming up. Hey, we're filing these charges." And they need to orchestrate complex operational processes across multiple software systems and multiple groups of people, again, kind of across jurisdictions or enclaves. And that was, of course, a really interesting systems analysis process that led to the development of a solution to this problem we were trying to assess, which we later called the Unified Justice Platform and is an event-driven architecture for building an entity-resolved knowledge graph as an operational data store programmatically as messages are exchanged between the stakeholders in the Enclave. Larry: Yeah. And you used a couple of words in there. I want to clarify for folks who might be new to them. The notion of entity resolution, the entity-resolved knowledge graph, I'll just point out that we met through our mutual friend, Paco Nathan, who works for Senzing, a company that just does entity resolution. And can you talk a little bit about entity resolution, how that fits into the needs of this distributed system and how you implement it in the platform? Brad: Yeah. Actually, I'll plug almost two years ago, we did a webinar with someone from Senzing and talked about the fundamental utility of entity resolution and relevance, I suppose, as a problem more generally. Entity resolution is essentially about creating, for me, is essentially about creating a high quality master index of whatever kind of data that it is that you're looking at. So in this case, we were talking about a master person index so that you have a more reliable picture of the same natural person, no matter which software system is representing the data that describes the person subject to judicial proceedings in particular. But thinking about entity-centric data modeling more generally, you got a different type of entity, you still need to disambiguate which location you're talking about, which person you're talking about, which entity that really is. And if there are different representations, different records that relate to the same underlying entity, that process of entity resolution therefore has this really broad systemic benefit to data management and data engineering in particular, because ultimately it's about the master index at the end of the day. Larry: Yeah. And as you talked about that, you mentioned that it's like this a canonical record of entities. And how does NIEM fit into that? Because that's a vocabulary as I understand it. Brad: That's right. Larry: Yeah. Can you talk a little bit about NIEM and how that works with entity resolution? Brad: Yeah, very briefly on NIEM, NIEM spun out of the post September 11th realization that public services needed to share data to collaborate more effectively to actually solve emergencies, but just problems in general. And what they realized was that they need to have a common language to collaborate more effectively. Again, because systems, machines, software systems, have this really concrete definition of we use these particular terms and they mean something in our enclave, but you could have a person's full name and a person's first name and a person's last name in two different records, but actually they're the same real person. So NIEM came out of an attempt to at least address some of that disambiguity. And what is most interesting to me about NIEM, honestly, is that it is a collaboratively defined list of vocabulary. So we actually get domain participants involved and they decide we use these terms and they mean these things. Brad: And so it's an attempt to reduce the amount of complexity that you could use to describe a different person, but communicate the same meaning without losing the information that's entailed in some data record. But I'm digressing a little bit probably. What NIEM is a framework for building message specifications, APIs, if you like, or other types of structures, data structures in general that is a community agreed-upon set of terms that have some kind of core relevance, person, entity, organization, or have some domain specific function, like, subject or something in human services and so on. Larry: Interesting. Yeah. And as you talk about that, that attempt to align people on vocabulary is such a notoriously difficult problem. And I don't know how many jurisdictions we're talking about here, but every little town in America has a police department and other social services that they do. What is the scope or the scale of that? And is it facilitated in any way by existing standards or vocabularies? Brad: Oh, very much so. In fact, the problem is even worse than you've described it very charitably, I think. Just in the United States alone, I'm told that there are over 18,000 law enforcement agencies, just law enforcement agencies. Nevermind how ... Anyway, so NIEM is a voluntary open standard. So it is something that is available, but is usually not mandated. There are some places where it is mandated for specific types of services. So the scale of the problem that we're talking about really depends on who's included in the conversation....
Dec 15, 2025 · 30 min
Tara Raafat: Human-Centered Knowledge Graph and Metadata Leadership – Episode 41
Tara Raafat At Bloomberg, Tara Raafat applies her extensive ontology, knowledge graph, and management expertise to create a solid semantic and technical foundation for the enterprise's mission-critical data, information, and knowledge. One of the keys to the success of her knowledge graph projects is her focus on people. She of course employs the best semantic practices and embraces the latest technology, but her knack for engaging the right stakeholders and building the right kinds of teams is arguably what distinguishes her work. We talked about: her history as a knowledge practitioner and metadata strategist the serendipitous intersection of her knowledge work with the needs of new AI systems her view of a knowledge graph as the DNA of enterprise information, a blueprint for systems that manage the growth and evolution of your enterprise's knowledge the importance of human contributions to LLM-augmented ontology and knowledge graph building the people you need to engage to get a knowledge graph project off the ground: executive sponsors, skeptics, enthusiasts, and change-tolerant pioneers the five stars you need on your team to build a successful knowledge graph: ontologists, business people, subject matter experts, engineers, and a KG product owner the importance of balancing the desire for perfect solutions with the pragmatic and practical concerns that ensure business success a productive approach to integrating AI and other tech into your professional work the importance of viewing your knowledge graph as not just another database, but as the very foundation of your enterprise knowledge Tara's bio Dr. Tara Raafat is Head of Metadata and Knowledge Graph Strategy in Bloomberg’s CTO Office, where she leads the development of Bloomberg’s enterprise Knowledge Graph and semantic metadata strategy, aligning it with AI and data integration initiatives to advance next-generation financial intelligence. With over 15 years of expertise in semantic technologies, she has designed knowledge-driven solutions across multiples domains including but not limited to finance, healthcare, industrial symbiosis, and insurance. Before Bloomberg, Tara was Chief Ontologist at Mphasis and co-founded NextAngles™, an AI/semantic platform for regulatory compliance. Tara holds a PhD in Information System Engineering from the UK. She is a strong advocate for humanitarian tech and women in STEM and a frequent speaker at international conferences, where she delivers keynotes, workshops, and tutorials. Connect with Tara online LinkedIn email: traafat at bloomberg dot net Video Here’s the video version of our conversation: https://youtu.be/yw4yWjeixZw Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 41. As groundbreaking new AI capabilities appear on an almost daily basis, it's tempting to focus on the technology. But advanced AI leaders like Tara Raafat focus as much, if not more, on the human side of the knowledge graph equation. As she guides metadata and knowledge graph strategy at Bloomberg, Tara continues her career-long focus on building the star-shaped teams of humans who design and construct a solid foundation for your enterprise knowledge. Interview transcript Larry: Hi everyone. Welcome to episode number 41 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Tara Raafat. She's the head of metadata and knowledge graph strategy at Bloomberg, and a very accomplished ontologist, knowledge graph practitioner. And welcome to the show, Tara. Tell the folks a little bit more about what you're doing these days. Tara: Hi, thank you so much, Larry. I'm super-excited to be here and chatting with you. We always have amazing chats, so I'm looking forward to this one as well. Well, as Larry mentioned, I'm currently working for Bloomberg and I've been in the space of knowledge graphs and ontology and creation for a pretty long time. So I've been in this community, I've seen a lot. And my interest has always been in the application of ontologies and knowledge graphs in industries, and have worked in so many different industries from banking and financial to insurance to medical. So I touched upon a lot of different domains with the application of knowledge graphs. And currently at Bloomberg, I am also leading their metadata strategy and the knowledge graph strategy, so basically semantic metadata. And we're looking over how we are basically connecting all the different data sources and data silos that we have within Bloomberg to make our data ready for all the AI interesting, exciting AI stuff that we're doing. And making sure that we have a great representation of our data. Larry: That's something that comes up all the time in my conversations lately is that people have done this work for years for very good reasons, all those things you just talked about, the importance of this kind of work in finance and insurance and medical fields and things like that. But it turns out that it makes you AI-ready as well. So is that just a happy coincidence or are you doing even more to make your metadata more AI-ready these days? Tara: Yeah. In a sense, you could say happy coincidence, but I think from the very beginning of when you think about ontologies and knowledge graphs, the goal was always to make your data machine-understandable. So whenever people ask me, "You're an ontologist, what does that even mean?" My explanation was always, I take all the information in your head and put it in a way that is machine understandable. So now encoded in that way. So now when we're thinking about the AI era, it's basically we're thinking if AI is operating on our information, on our data, it needs to have the right context and the right knowledge. So it becomes a perfect fit here. So if data is available and ready in your knowledge graph format, it means that it's machine understandable. It has the right context. It has the extra information that an AI system, specifically in the LLM era and generative AI needs in order to make sure that the answering that it's done is more grounded and based in facts, or have a better provenance. And it's more accurate in quality. Larry: Yeah, that's right. You just reminded me, it's not so much serendipity or a happy coincidence. It's like, no, it's just what we do. Because we make things accessible. The whole beauty of this is the- Tara: We knew what's coming, right? The word AI has changed so much. It's the same thing. It just keeps popping up in different contexts, but yeah. Larry: So you're actually a visionary futurist as all of us are in the product. Yeah. In your long experience, one of the things I love most, there's a lot of things I love about your work. I even wrote about it after KGC. I summarized one of your talks, and I think it's on your LinkedIn profile now, you have this great definition of a knowledge graph. And you liken it to a biological concept that I like. So can you talk a little bit about that? Tara: Sure. I see knowledge graph as the DNA of data or DNA of our information. And the reason I started thinking about it that way is when you think about the human DNA, you're literally thinking of the structure and relationship of the organisms and how they operate and how they evolve. So there's a blueprint of their operation and how they would grow and evolve. And for me, that's very similar to when we start creating a knowledge graph representation of our data, because we're again, capturing the structure and relationships between our data. And we're actually encoding the context and the rules that are needed to allow our data to grow and evolve as our business grows and evolves. So there's a very similarity for me there. And it also brings that human touch to this whole concept of knowledge graphs because when I think about knowledge graphs and talking about ontologies, it comes from a philosophical background. And it's a lot more social and human. Tara: And at the end of the day, the foundation of it is how we as humans interpret the world and interpret information. And how then by the use of technology, we encode it, but the interpretation is still very human. So that's why this link for me is actually very interesting. And I think one more thing I would add, which is I do this comparison to also emphasize on the fact that knowledge graphs are not just another database or another data store. So I don't like companies to look at it from that perspective. They really should look at it as the foundation on which their data grows and evolves as their business grows. Larry: Yeah. And that foundational role, it just keeps coming up, again, related to AI a lot, the LLM stuff that I've heard a lot of people talk about the factual foundation for your AI infrastructure and that kind of thing. And again, another one of those things like, yeah, it just happens to be really good at that. And it was purpose built for that from the start. Larry: You mentioned a lot in there, the human element. And that's what I was so enamored of with your talk at KGC and other talks you've done and we've talked about this. And one of the things that, just a quick personal aside, one of the things that drives me nuts about the current AI hype cycle is this idea like, "Oh, we can just get rid of humans. It's great. We'll just have machines instead." I'm like, "Have you not heard..." Every conversation, I've done about 300 different interviews over the years. Every single one of them talks about how it's not technical, it's not procedural or management wisdom. It's always people stuff. It's like change management and working with people. Can you talk about how the people stuff manifests in your work in metadata strategy and knowledge graph construction? I know that's a lot. Tara: Sure. I think there are different aspects to it and we can choose to talk abo
Nov 3, 2025 · 31 min
Alexandre Bertails: The Netflix Unified Data Architecture – Episode 40
Alexandre Bertails At Netflix, Alexandre Bertails and his team have adopted the RDF standard to capture the meaning in their content in a consistent way and generate consistent representations of it for a variety of internal customers. The keys to their system are a Unified Data Architecture (UDA) and a domain modeling language, Upper, that let them quickly and efficiently share complex data projections in the formats that their internal engineering customers need. We talked about: his work at Netflix on the content engineering team, the internal operation that keeps the rest of the business running how their search for "one schema to rule them all" and the need for semantic interoperability led to the creation of the Unified Data Architecture (UDA) the components of Netflix's knowledge graph Upper, their domain modeling language their focus on conceptual RDF, resulting in a system that works more like a virtual knowledge graph his team's decision to "buy RDF" and its standards the challenges of aligning multiple internal teams on ontology-writing standards and how they led to the creation of UDA their two main goals in creating their Upper domain modeling language - to keep it as compact as possible and to support federation the unique nature of Upper and its three essential characteristics - it has to be self-describing, self-referencing, and self-governing their use of SHACL and its role in Upper how his background in computer science and formal logic and his discovery of information science brought him to the RDF world and ultimately to his current role the importance of marketing your work internally and using accessible language to describe it to your stakeholders - for example describing your work as a "domain model" rather than an ontology UDA's ability to permit the automatic distribution of semantically precise data across their business with one click how reading the introduction to the original 1999 RDF specification can help prepare you for the LLM/gen AI era Alexandre's bio Alexandre Bertails is an engineer in Content Engineering at Netflix, where he leads the design of the Upper metamodel and the semantic foundations for UDA (Unified Data Architecture). Connect with Alex online LinkedIn bertails.org Resources mentioned in this interview Model Once, Represent Everywhere: UDA (Unified Data Architecture) at Netflix Resource Description Framework (RDF) Schema Specification (1999) Video Here’s the video version of our conversation: https://youtu.be/DCoEo3rt91M Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 40. When you're orchestrating data operations for an enormous enterprise like Netflix, you need all of the automation help you can get. Alex Bertails and his content engineering team have adopted the RDF standard to build a domain modeling and data distribution platform that lets them automatically share semantically precise data across their business, in the variety of formats that their internal engineering customers need, often with just one click. Interview transcript Larry: Hi, everyone. Welcome to episode number 40 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show, Alex Bertails. Alex is a software engineer at Netflix, where he's done some really interesting work. We'll talk more about that later today. But welcome, Alex, tell the folks a little bit more about what you're up to these days. Alex: Hi, everyone. I'm Alex. I'm part of the content engineering side of Netflix. Just to make it more concrete, most people will think about the streaming products, that's not us. We are more on the enterprise side, so essentially the people helping the business being run, so more internal operations. I'm a software engineer. I've been part of the initiative called UDA for a few years now, and we published that blog post a few months ago, and that's what most people want to talk about. Larry: Yeah, it's amazing that the excitement about that post and so many people talking about it. But one thing, I think I inferred it from the article, but I don't recall a real explicit statement of the problem you were trying to solve in that. Can you talk a little bit about the business prerogatives that drove you to create UDA? Alex: Yeah, totally. There was no UDA, there's no clear problem that we had to solve and really people, won't realize that, but we've been thinking about that point for a very long time. Essentially, on the enterprise side, you have to think about lots of teams having to represent the same business concepts, think about movie actor region, but really hundreds of them really, across different systems. It's not necessarily people not agreeing on what a movie is, although it happens, but it's really what is the movie across a GraphQL service, a data mesh source, an Iceberg table, resulting in duplicating efforts and definitions at the end not aligning. A few years ago, we were in search for this one schema kind of concept that would actually rule them all, and that's how we got into domain modeling, and how can we do that kind of domain modeling across all representations? Alex: So there was one part of it. The other part is we needed to enable what's called semantic interoperability. Once we have the ability to talk about concepts and domain models across all of the representations, then the next question is how can we actually move and help our users move in between all of those data representations? There is one thing to remember from the article that's actually in the title, that's that concept of model once, represent everywhere. The core idea with all of that is to say once we've been able to capture a domain model in one place, then we have the ability to project and generate consistent representations. In our case, we are focused on GraphQL, Avro, Java, and SQL. That's what we have today, but we are looking into adding more support for other representations. Larry: Interesting. And I think every enterprise will have its own mix of data structures like that that they're mapping things to. I love the way you use the word, project. I think different people talk about what they do with the end results of such systems. You have two concepts you talk about as you talk about this, the notion of mappings, which we're just talking about with the data stuff, but also that notion of projection. That's sort of like once you've instantiated something out this system, you project it out to the end user. Is that kind of how it works? Alex: Yes, so we do use the term, projection, in the more mathematical sense, and more people would call that denotations. So essentially, once you have a domain model, and you can reason about it, and we have actually, a formal representation of the domain models, maybe we'll talk about that a little bit later. But then you can actually define how it's supposed to look like, the exact same thing with the same data semantics, but as an API, for example, in GraphQL, or as a data product in Iceberg, in the data warehouse, or as a low-compacted Kafka topic in our data mesh infrastructure as Avro. So for us, we have to make sure that it's quote, unquote, "the same thing," regardless of the data representation that the user is actually interested in. Alex: To put everything together, you talked about the mappings, what's really interesting for us is that the mappings are just one of the three main components that we have in our knowledge graph, because at the end of the day, UDA at its core is really a knowledge graph which is made out of the domain models. We've talked about that. Then the mappings, the mappings are themselves objects in that knowledge graph, and they are here actually to connect the world of concepts from the domain models through the worlds of data containers, which in our case could represent things like an Iceberg table, so we would want to know the coordinates on the Iceberg table and we would want to know the schema. But that applies as well to the data mesh source abstraction and the Avro schema that goes with it. Alex: That would apply as well, and that's a tricky part that very few people actually try to solve, but that would apply to the GraphQL APIs. We want to be able to say and know, oh, there is a type resolver for that GraphQL type that exists in that domain graph service and it's located exactly over there. So that's the kind of granularity that we actually capture in the knowledge graph. Larry: Very cool. And this is the Knowledge Graph Insights podcast, which is how we ended up talking about this. But that notion of the models, and then the mappings, and then the data containers that actually have everything, I'm just trying to get my head around the scale of this knowledge graph. You said this is not just, but you tease it out, it doesn't have to do with the streaming services or the customer facing part of the business, it's just about your kind of content and data media assets that you need to manage on the back end. Are you sort of an internal service? Is that how it's conceived or? Alex: That's a good question. So we are not so much into the binary data. That's not at all what UDA is about. Again, it's knowledge graph podcast, for sure, but even more precisely, when we say knowledge graph, we really mean conceptual RDF and we are very, very clear about that. That means for us, quite a few things. The knowledge graph, in our case, needs to be able to capture the data wherever it lives. We do not want necessarily to be RDF all the way through, but at the very core of it, there is a lot of RDF. I'm trying to remember how we talk about it. But yeah, so think about a graph representation of connected data. And again, it has to work across all of the data representations, but we want to make sure that we have enough information about t
Oct 12, 2025 · 32 min
Torrey Podmajersky: Aligning Language and Meaning in Complex Systems – Episode 39
Torrey Podmajersky Torrey Podmajersky is uniquely well-prepared to help digital teams align on language and meaning. Her father's interest in philosophy led her to an early intellectual journey into semantics, and her work as a UX writer at companies like Google and Microsoft has attuned her to the need to discover and convey precise meaning in complex digital experiences. This helps her span the "semantic gaps" that emerge when diverse groups of stakeholders use different language to describe similar things. We talked about: her work as president at her consultancy, Catbird Content, and as the author of two UX books how her father's interest in philosophy and semantics led her to believe that everyone routinely thinks about what things mean and how to represent meaning the role of community and collaboration in crafting the language that conveys meaning how the educational concept of "prelecting" facilitates crafting shared-meaning experiences the importance of understanding how to discern and account for implicit knowledge in experience design how she identifies "semantic gaps" in the language that various stakeholders use her discovery, and immediate fascination with, the Cyc project and its impact on her semantic design work her take on the fundamental differences between how humans and LLMs create content Torrey's bio Torrey Podmajersky helps teams solve business and customer problems using UX and content at Google, OfferUp, Microsoft, and clients of Catbird Content. She wrote Strategic Writing for UX, is co-authoring UX Skills for Business Strategy, hosts the Button Conference, and teaches content, UX, and other topics at schools and conferences in North America and Europe. Connect with Torrey online LinkedIn Catbird Content (newsletter sign-up) Torrey's Books Strategic Writing for UX UX Skills for Business Strategy Resources mentioned in this interview Cyc project Button Conference UX Methods.org Video Here’s the video version of our conversation: https://youtu.be/0GLpW9gAsG0 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 39. Finding the right language to describe how groups of people agree on the meaning of the things they're working with is hard. Torrey Podmajersky is uniquely well-prepared to meet this challenge. She was raised in a home where where it was common to have philosophical discussions about semantics over dinner. More recently, she's worked as a designer at tech companies like Google, collaborating with diverse teams to find and share the meaning in complex systems. Interview transcript Larry: Hi everyone. Welcome to episode number 39 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Torrey Podmajersky. I've known Torrey for years from the content world, the UX design and content design and UX writing and all those worlds. I used to live very closer to her office in Seattle, but Torrey's currently the president at Catbird Content, her consultancy, and she's guest faculty at the University of Washington iSchool. She does all kinds of interesting stuff, very accomplished author. So welcome Torrey. Tell the folks a little bit more about what you're up to and where all the books are at these days. Torrey: Thanks so much, Larry. I am up to my neck in finishing the books right now. So one just came out the second edition of Strategic Writing for UX that has a brand new chapter on building LLMs into products and updates throughout, of course since it came out six years ago. But I'm also working on the final manuscript with twoTorrey Podmajersky co-authors for UX Skills for Business Strategy. That'll be a wine pairing guide, a deep reference book that connects the business impact that you might want to make, whether you're a UX pro or a PM or a knowledge graph enthusiast working somewhere in product and connecting it to the UX skills you might want to use to make those impacts. Larry: Excellent. I can't wait to read both of those. I love the first edition of the Strategic Writing for UX book, but... Hey, I want to talk today though about, this is the Knowledge Graph Insights podcast, and you recently did this great post and we'll talk more about it in detail in a bit about how you had discovered the Cyc project, which is a real pioneering project in the semantic technology field and really foundational to a lot of the knowledge graph stuff that's happening today. But I want to start with one of the other things we talked about before we went on the air was your observation of the kind of common philosophical roots that we have in rhetoric, maybe not necessarily rhetoric, but the stuff that we do as word nerds, as meaning nerds, as all these different kinds of technology nerds that we are. Tell me a little bit about what you meant because you just hinted that and I was like, oh, good philosophy. I love philosophy. Torrey: Yeah, I love philosophy too, especially through my dad. My dad was a philosophy major at Haverford College and it has deeply influenced his life and his work in semantic knowledge spaces. And I got to grow up in that context thinking that everybody thought deeply about what things meant and how we represent those meanings. I mean, the Plato's Allegory of the Cave was my bedtime story to the extent that we all knew Plato in the cave, geez, dad, just fine. Plato in the cave. We don't really know anything. All we have is facsimiles and representations of meaning and representations of reality, and through that we construct meaning. And I feel like that's all we're ever doing is using language to construct meaning based on our inability to fully perceive reality. Larry: And just for folks who aren't familiar, I love Plato's Allegory of the Cave. It's these poor people chained to a wall and behind them is a projector projecting stuff on the wall in front of them. So all they see is this projection of an imitation of reality, which is much like what we're doing with either both UX writing and I think ontology design and semantic engineering. So that's the perfect analogy to come into this. But your job for the last, I don't know, because you made the transition from teaching to Xbox, what? 10, 12 years ago or something like that? Torrey: In 2010, I joined Xbox and before that I had a short stint in internal communications in a division at Microsoft working for a VP there. Larry: But you've been in the word biz and the meaning biz for a long time because UX writing is, how did you say it? You have to convey meaning. That's the whole point of UX writing is to just get past random words to actually, what are we talking about here? Torrey: It's to make the words that people understand so quickly while they're in an experience, they're just trying to use it. They're not there to read. So we want the words to disappear into ephemeral meaning in their head that they don't even remember. They just knew what to do and which button to press and where to go next to get done what they wanted to get done. Larry: And one of the things about that is getting to that language to do that in an experience, that's a team sport. One of the other things that really struck me about that post you did was the role of community in language and meaning. Talk a little bit about that. Torrey: Yeah, it is a team sport because in general, even if it's the person doing the UX writing or that content design is also the product designer is also the interaction designer. What they're trying to do is take a wide variety of people who might be using this product that might be an incredibly diverse set of people, or it might be a very narrow set of people, let's say all IT pros. We want to sell this product to big corporations that have IT pros that want to manage their data centers. It's a pretty narrow slice of humans, but it's still hugely diverse in terms of from what language they're speaking and what kind of resources they have inside this company to the kind of background they have, to all of the different reasons they might need to manage their data centers right now. Torrey: From, hey, something new came online or there needs to be a new partition or new admin management of access to it or security patch updates to things like, oh, there was an earthquake at a data center and I need to and secure and audit any damage that might've happened. So there's a huge number of reasons. Let me back up of that deep analogy. There's a huge number of reasons even for a tiny population relative to the scope of humanity, a small population doing a relatively well-defined job still has a huge number of reasons they might need to be in an interface doing a thing. And what we have to do when we are designing the content for that and designing the experience itself is anticipate those and try and make sure that we've indicated that whatever reason they're coming there for, if it's a valid reason to use this piece of software, whatever reason they're coming there for, they see it reflected in the text and they understand what to do. Torrey: That is a team sport because I can't, and no individual person can anticipate all of those things simultaneously. We need to think them through sequentially. We need data to base it on. We need to understand, we need to hear from people who will use it or people who would use it to hear about how they think about it and specifically what language do they use, what's already in their head that we can use to reflect on that screen. So it's about understanding that space well enough, coming to understand that space well enough by communicating with other humans to know what are the right things to represent and in what hierarchy or embeddedness or relationalness, and then use some grammar and punctuation and other tricks up our language sleeves. Larry: Yeah, no....
Aug 20, 2025 · 39 min
Casey Hart: The Philosophical Foundations of Ontology Practice – Episode 38
Casey Hart Ontology engineering has its roots in the idea of ontology as defined by classical philosophers. Casey Hart sees many other connections between professional ontology practice and the academic discipline of philosophy and shows how concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology in general. We talked about: his work as a lead ontologist at Ford and as an ontology consultant his academic background in philosophy the variety of pathways into ontology practice the philosophical principles like metaphysics, epistemology, and logic that inform the practice of ontology his history with the the Cyc project and employment at Cycorp how he re-uses classes like "category" and similar concepts from upper ontologies like gist his definition of "AI" - including his assertion that we should use term to talk about a practice, not a particular technology his reminder that ontologies are models and like all models can oversimplify reality Casey's bio Casey Hart is the lead ontologist for Ford, runs an ontology consultancy, and pilots a growing YouTube channel. He is enthusiastic about philosophy and ontology evangelism. After earning his PhD in philosophy from the University of Wisconsin-Madison (specializing in epistemology and the philosophy of science), he found himself in the private sector at Cycorp. Along his professional career, he has worked in several domains: healthcare, oil & gas, automotive, climate science, agriculture, and retail, among others. Casey believes strongly that ontology should be fun, accessible, resemble what is being modelled, and just as complex as it needs to be. He lives in the Pacific Northwest with his wife and three daughters and a few farm animals. Connect with Casey online LinkedIn ontologyexplained at gmail dot com Ontology Explained YouTube channel Video Here’s the video version of our conversation: https://youtu.be/siqwNncPPBw Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 38. When the subject of philosophy comes up in relation to ontology practice, it's typically cited as the origin of the term, and then the subject is dropped. Casey Hart sees many other connections between ontology practice and it its philosophical roots. In addition to logic as the foundation of OWL, he shows how philosophy concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology in general. Interview transcript Larry: Hi, everyone. Welcome to episode number 38 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Casey Hart. Casey has a really cool YouTube channel on the philosophy behind ontology engineering and ontology practice. Casey is currently an ontologist at Ford, the motor car company. So welcome Casey, tell the folks a little bit more about what you're up to these days. Casey: Hi. Thanks, Larry. I'm super excited to be here. I've listened to the podcast, and man, your intro sounds so smooth. I was like, "I wonder how many edits that takes." No, you just fire them off, that's beautiful. Casey: Yeah, so like you said, these days I'm the ontologist at Ford, so building out data models for sensor data and vehicle information, all those sorts of fun things. I am also working as a consultant. I've got a couple of different startup healthcare companies and some cybersecurity stuff, little things around the edge. I love evangelizing ontology, talking about it and thinking about it. And as you mentioned for the YouTube channel, that's been my creative outlet. My background is in philosophy and I was interested in, I got my PhD in philosophy, I was going to teach it. You write lots of papers, those sorts of things, and I miss that to some extent getting out into industry, and that's been my way back in to, all right, come up with an idea, try and distill it, think about objections, put it together, and so I'm really enjoying that lately. Larry: And I'm enjoying the video- Casey: Glad to be on the show. Larry: Yeah, no, I really appreciate what you're doing there. One thing I wanted to, and I love that that's how you're getting back to both your philosophical roots, but also part of it is to evangelize ontology practice, which is that's what this podcast is all about, democratizing and sharing practice. But I think, and I just love that you have this explicit and strong philosophical foundation and bent to how you talk about things. I think a lot of times that conversation is like, "Yeah, ontology comes out of philosophy," and that's the end of the conversation. But you've mentioned the role of metaphysics, epistemology, logic, all of which, can you talk a little bit about how those, beyond just I think a lot of people think about logic and OWL and all that stuff, but can you talk a little bit more about the role of metaphysics and epistemology and these other philosophical ideas? Casey: Yeah, definitely. You mentioned this in the pre-notes, "Here's a topic we'd like to get to," and I got into a lot of imposter syndrome on this, right? I'm trying to talk myself out of this, but I think most ontologists have this feeling there's no solid easy pipeline into becoming an ontologist, right? It's a very eclectic group of us. My background's in philosophy, you run into a bunch of librarians, you've got computer scientists who do DB administration, you've got jazz musicians I've run into, it's a weird group. Casey: I say that just to be, sometimes when I get asked about, "Okay, how does ontological practice work?" I think, well, I didn't actually train to be an ontologist. I fell into it, so I'm ill-equipped to say things about what role ontology or philosophy plays in ontology. Casey: I just know I learned philosophy, and then I'm using some of those tools here, so there's two different answers. One is historically, how does philosophy inform and shape the nature of ontology practice? And the other part is just, okay, if you've got a philosophical toolkit of metaphysics and epistemology and logic, how does that apply and make you a better, I mean, the obvious connection is that ontology is a philosophical term. It comes from metaphysics. We look back to Aristotle, and it's the study of that which exists, so do we want to say there's fundamentally fire, air, earth, water or something like that? Or fundamentally, there are these atoms and those are the sorts of things that are part of the inventory of reality. It's not physics, it's metaphysics. It's the thing that in I think for Aristotle is just, it's the book that sits next to his physics in all of his category, in his library of everything. Casey: But when we move that forward to computer science and data modeling, then we're thinking, okay, maybe not for all of reality, although maybe it depends on how big you want your data model to be. But if I'm a retailer, what are the terms and ontology, what are the terms that I care about, the things that I need to model the constituents of reality that matter to me? That might be types, if you're Amazon, it's okay, medium-sized dry goods versus sporting equipment versus something else. If I'm doing a medical ontology, it's patients and payers and providers, et cetera. In philosophy, in ontology, there's a bunch of different tools and examples, but we think about, okay, what are some fundamental distinctions that we want to make? How can we carve nature at its joints in really sensible ways? That's a phrase that you'll hear a lot. We could say more about it if you want. Casey: But what I found is being a philosopher goes into an ontology space is that I have this inventory of examples from all of my grad seminars and various things that I'm looking through and going through whether I want to talk about gavagai and undetached rabbit parts, if that makes sense to anybody, or whether I want to talk about grue as a color, here are some examples, ways that we can chop up the world in unnatural ways versus chopping it up in natural ways and how do we make those distinctions? That applies straightforwardly when you get into building an ontology model for an oil and gas industry or something like that. There's a bunch of ways that we can divvy up all the things you care about, what's the right and sensible way to do it? Casey: I guess that's the metaphysics, ontology way. Logic you mentioned, right? We need to think about reasoning. I don't just want to assert a bunch of things about my data. A fundamental premise of an ontology is that we want to understand our data, we want to confer meaning on it, and that means that we have to be able to leverage the structure of the ontology to infer things smartly. Simple things like set containment are fine if all persons are animals, and then we say something about animals, they're creatures. Then when I say that persons are a subclass of that, then I get for free that persons are spatio-temporal things as well. But we get a lot more complicated inferences as we go. We have to think about statistical reasoning. Just in general, if logic is the study of what makes for good arguments, what follows from what, that's obviously got a lot of applications in ontology, AI. Casey: And then the third piece that we talked about is epistemology. Epistemology is the study of knowledge and belief, roughly about what it means to be justified. The classic example there is, if I know something, what exactly does that amount to? And then Plato says it's justified true beliefs. And then the history of epistemology is littered with examples of trying to cash out exactly what does it mean to be justified. And if you get new information, how can that undercut your justifications? How do you update your beliefs? Casey: More recent stuff, and this is what I did in my dissertation,...
Aug 4, 2025 · 32 min
Chris Mungall: Collaborative Knowledge Graphs in the Life Sciences – Episode 37
Chris Mungall Capturing knowledge in the life sciences is a huge undertaking. The scope of the field extends from the atomic level up to planetary-scale ecosystems, and a wide variety of disciplines collaborate on the research. Chris Mungall and his colleagues at the Berkeley Lab tackle this knowledge-management challenge with well-honed collaborative methods and AI-augmented computational tooling that streamlines the organization of these precious scientific discoveries. We talked about: his biosciences and genetics work at the Berkeley Lab how the complexity and the volume of biological data he works with led to his use of knowledge graphs his early background in AI his contributions to the gene ontology the unique role of bio-curators, non-semantic-tech biologists, in the biological ontology community the diverse range of collaborators involved in building knowledge graphs in the life sciences the variety of collaborative working styles that groups of bio-creators and ontologists have created some key lessons learned in his long history of working on large-scale, collaborative ontologies, key among them, meeting people where they are some of the facilitation methods used in his work, tools like GitHub, for example his group's decision early on to commit to version tracking, making change-tracking an entity in their technical infrastructure how he surfaces and manages the tacit assumptions that diverse collaborators bring to ontology projects how he's using AI and agentic technology in his ontology practice how their decision to adopt versioning early on has enabled them to more easily develop benchmarks and evaluations some of the successes he's had using AI in his knowledge graph work, for example, code refactoring, provenance tracking, and repairing broken links Chris's bio Chris Mungall is Department Head of Biosystems Data Science at Lawrence Berkeley National Laboratory. His research interests center around the capture, computational integration, and dissemination of biological research data, and the development of methods for using this data to elucidate biological mechanisms underpinning the health of humans and of the planet. He is particularly interested in developing and applying knowledge-based AI methods, particularly Knowledge Graphs (KGs) as an approach for integrating and reasoning over multiple types of data. Dr. Mungall and his team have led the creation of key biological ontologies for the integration of resources covering gene function, anatomy, phenotypes and the environment. He is a principal investigator on major projects such as the Gene Ontology (GO) Consortium, the Monarch Initiative, the NCATS Biomedical Data Translator, and the National Microbiome Data Collaborative project. Connect with Chris online LinkedIn Berkeley Lab Video Here’s the video version of our conversation: https://youtu.be/HMXKFQgjo5E Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 37. The span of the life sciences extends from the atomic level up to planetary ecosystems. Combine this scale and complexity with the variety of collaborators who manage information about the field, and you end up with a huge knowledge-management challenge. Chris Mungall and his colleagues have developed collaborative methods and computational tooling that enable the construction of ontologies and knowledge graphs that capture this crucial scientific knowledge. Interview transcript Larry: Hi everyone. Welcome to episode number 37 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Chris Mungall. Chris is a computational scientist working in the biosciences at the Lawrence Berkeley National Laboratory. Many people just call it the Berkeley Lab. He's the principal investigator in a group there, has his own lab working on a bunch of interesting stuff, which we're going to talk about today. So welcome, Chris, tell the folks a little bit more about what you're up to these days. Chris: Hi, Larry. It's great to be here. Yeah, so as you said, I'm here at Berkeley Lab. We're located in the Bay Area. We're just above UC Berkeley campus. We have a nice view of the San Francisco Bay looking into San Francisco, and so we're a national lab, so we're part of the Department of Energy National Lab system, and we have multiple different areas here in the lab looking at different aspects of science from physics, energy technologies, material science. I'm in the biosciences area, so we are really interested in how we can advance biological science in areas relevant to national scale challenges really in different areas like energy, the environment, health and bio-manufacturing. Chris: My own particular research is really focused on the role of genes and in particular the role of genes in complex systems. So this could be the genes that we have in our own cells, the genes in human beings, how they all work together to hopefully create a healthy human being. One part of my research also looks at the role of genes in the environment, and in particular the role of genes inside tiny old microbes that you'll find in the ocean water and in the soil. And how these genes all work together, both to help drive these microbial systems, help them work together and how they all work together really to drive ecosystems and biogeochemical cycles. Chris: So I think the overall aim is really just to get a picture of these genes and how they interact in these kind of complex systems and build up models of complex systems from scales right the way from atoms through the way through to organisms and indeed all the way to earth-scale systems. So my work is all computational. I don't have a wet lab. So one thing that we realized early on is just when you are sequencing these genomes and trying to interpret the genes, you're generating a lot of information and you need to be able to organize that somehow. And so that's how we arrived at working on knowledge graphs, basically to assemble all of this information together and to be able to use it in algorithms to help us interpret biological data and help us figure out the role of genes in these organisms. Larry: Yeah, many of the people I've talked to on this podcast, they come out of the semantic technology world and apply it in some place or another. It sounds like you came to this world because of the need to work with all the data you've got. What was your learning curve? Was it just another thing in your computational toolkit? Chris: Yeah, in some ways. In fact, my background is, if you go back far enough, my original background is more on the computational side and my undergrad was in AI, but this is back when AI meant good old-fashioned AI and symbolic reasoning and developing Prolog rules to reason about the world and so on. And at that time, I wasn't so interested in that side of AI. I really wanted to push forward with some of the more nascent neural network type approaches. But in those days, we didn't really have the computational power and I thought, "Well, maybe I really need to, I actually learned something about biological systems before trying to simulate them." So that's how I got involved in genomics. This was around about the time of just before the sequencing of the human genome, and I just got really interested in this area, a position came up here at Lawrence Berkeley National Laboratory, and I just got really involved in analyzing some of these genomes. Chris: And in doing this, I came across this project called the Gene Ontology that was developed by some of my colleagues originally in Cambridge and at Lawrence Berkeley National Laboratory. And the goal here was really as we were sequencing these genomes and we were figuring out there's 20,000 genes in the human genome, we discovered we had no way to really categorize what the functions of these different genes were. And if you think about it, there's multiple different ways that you can describe the function of any kind of machine, whether it's a molecular machine inside one of your cells or your car or your iPhone or whatever. You can describe it in terms of what the intent of that machine is. You can describe it in terms of where that machine is localized and what it does, and how that machine works as part of a larger ensemble of machines to achieve some larger objective. Chris: So my colleagues came up with this thing called the gene ontology, and I looked at that and I said, "Hey, I've got this background in symbolic reasoning and good old-fashioned AI. Maybe I could play a role in helping organize all of this information and figuring out ways to connect it together as part of a larger graph." We didn't call them knowledge graphs at this time, but we're essentially building knowledge graphs at the time and make use of, in those days quite early semantic web technologies. This is even before the development of all the web ontology language, but there was still this notion that we could use, we could use rules in combination with graphs to make inferences about things. And I thought, "Well, this seems like an ideal opportunity to apply some of this technology." Larry: That's interesting. It's funny we didn't plan this, but the episode right before you in the queue was of my friend Emeka Okoye. He's a guy who was building knowledge graphs in the late '90s, early 2000s, mostly the early 2000s before the term had been coined, and I think maybe even before a lot of the RDF and OWL and all that stuff was there. So you mentioned Prolog earlier, and what was your toolkit then, and how has it evolved up to the present? That's a huge question. Yeah. Chris: I didn't mean to get into my whole early days with Prolog. Yeah, I've definitely had some interest in applying a lot of these logic programming technologies. As you're aware,...

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