Skip to content
Sign in
Artwork for Knowledge Graph Insights
TechnologyBusinessManagementNewsTech News

Knowledge Graph Insights

Larry Swanson

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

Play
Open in
  • 23 episodes
  • fortnightly
  • Avg 35 min
  • English

Support the show

Goes straight to the publisher. podnod takes nothing.

Episodes 23

NewestOldestLongest
All3Unplayed3In progress0Played0
Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • 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,...

Showing 21–23 of 23 episodes