Knowledge Graph Insights

David Newbury Modeling data and knowledge always involves trade-offs. At the Getty Trust, David Newbury manages what he calls a “complexity budget,” focusing on the concepts his users are most concerned with. For example, they carefully model time for art historians while downplaying geospatial data. Getty shares the resulting structured knowledge in two ways: as simple reference documents and as a rich knowledge graph that helps researchers answer questions that no one has asked before. We talked about: his role as Senior Director of Public Technologies at the Getty Trust how Getty settled on semantic technology to manage and share knowledge about its extensive collections the differences between how an organization like Getty thinks about its collections and how its users access that information the unique benefits that an independent cultural heritage organization has over commercial entities when it comes to knowledge architectures his academic work on "pointers and proxies" — knowledge organization that helps users find things vs. metadata and other information that lets researchers digitally model items in their collection to conduct research the philosophy and practical considerations that underlie their modeling practices their allocation of a good portion of their "complexity budget" to modeling time, since that is such important information for art historians and other researchers the crucial role of linking in their metadata and modeling strategy the origin of the concept of provenance in the field of art history the two different ways they talk about provenance at Getty: their Provenance Index, a huge collection of art-transaction receipts, and their historical records of the people, places, and things collected their goal to serve as a source of ground truth about objects in their collections, providing the same information both in reference documents and in complex graphs that let people ask research questions that Getty staff may not have thought about the broad adoption of their Getty Vocabularies, a collection of cataloging taxonomies that are a standard across the GLAM space, and their upcoming updates David's bio David Newbury is Senior Director, Public Technologies at Getty, where he leads the team responsible for using technology to share Getty’s knowledge, collections, and campuses with the world. He oversees digital imaging, collections platforms & data, and digital visitor experience, and has led Getty’s transition to a linked-data first organization, working across teams to develop data infrastructure and access mechanisms for Getty’s knowledge and collections. David is the former president of MCN, on the executive committee for the IIIF consortium, and one of the original creators of the Linked.Art standard. Prior to his time at Getty, David's work included Art Tracks, a provenance data standards project with the Carnegie Museum of Art; and the American Art Collaborative, working with 14 museums to connect collections through linked data. He formerly worked with Carnegie Mellon, American Eagle Outfitters, the University of British Columbia, and PBS. Connect with David online LinkedIn email: dnewbury at getty dot edu Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 59. Modeling data and knowledge always involves trade-offs. At the Getty Trust, David Newbury manages what he calls a “complexity budget,” focusing on the concepts his users are most concerned with — for example, carefully modeling time for art historians while downplaying geospatial data. Getty then shares that knowledge in two ways — as simple reference documents and as a rich knowledge graph that helps researchers answer questions that no one has asked before. Interview transcript Larry: Hi, everyone. Welcome to episode number 59 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, David Newbury. David's the Senior Director of Public Technologies at the J. Paul Getty Trust. Welcome, David. Tell the folks a little bit more about what you're up to these days. David: Hi, as you said, I'm David Newbury. I work for Getty, which is a museum/library/research institute out in Los Angeles. It's always important for me to start these things by saying that I am not part of Getty Images. It turns out that generational wealth tends to create lots of institutions and they name them all after themselves, but we are a museum and an archive that collects data and shares out with Los Angeles and the world. We don't sell stock photos. It's always confusing because we do collect photos, and so there are millions and millions of photos here, but not those ones. Larry: Got it. Yeah, no, I think it's always important to get that out there. David: Yeah. And I have to start every presentation that way because no matter how many times I say it, someone asks me about royalty images. Larry: Can you get me a deal on... I want to just briefly tell folks how we connected. I was at the Semantic Data Summit in New York about three or four weeks ago, and you did this really great presentation about your work at Getty. And one of the things you talked about there is you have all these things, you have collections of things, and you opened with this example of you had an illustrated manuscript, a photo album, and a book of poems, and at any one point in time, each of those things, it's a piece of art, it's a research source, it's a collection in its own right. Can you tell me a little bit about why you opened your presentation with that and how these sort of perspectives influence your work? David: So one of the reasons Getty got very excited by semantic technology is we always have this idea that it would be really interesting to understand the connections between all the stuff that we have here, because Getty does two things. It collects things and we have warehouses full of things, sort of Indiana Jones style, and then we generate knowledge about those things. We have hundreds of people here who are the best in their field who know about pottery or know about 17th century silver making, and they create knowledge about that. And so we know huge amounts of stuff about the stuff that we collect, but we tend to know it through the lens of the various disciplines that generate knowledge. And so we have a bunch of librarians who are really good at thinking about the way that you catalog books and the way you capture those. David: We have a bunch of archivists who are really good at managing collections of ephemera, papers and receipts and the stuff that humans collect over their life. We have curators who describe artwork, but at the end, they're all talking about things that we have on a shelf somewhere, generating knowledge about those structured in really different ways, but that are all connected to this larger idea of how do humans create material culture and how do we talk about it and how does it influence our society? David: When we are thinking about what we are doing, it is taking that data and trying to do something to make it useful and useful in the aggregate. And so we often have to go not... My job, I think, when I'm thinking about our data infrastructure is really to say, how do we go below the level of those disciplinary practices and figure out a way that we can talk across all of those infrastructures? What are the connecting points between them? And how do we make those connecting points visible and how to make those connecting points interesting as a way to build interfaces and applications and generate new knowledge on top of it? Larry: That kind of gets at what we were talking just before we went on the air about in another context, but that your charter there is about really getting this batch of knowledge ready for other people to use. So you have a website, there's a way to access the work you do via your own thing, but this sounds like this is mostly about creating a shareable, reusable, connected set of both the information about the artifacts and then all the cultural stuff that goes around them. David: Yeah, it turns out that most of the interesting questions that people want to ask about art, like the Getty's collection does not answer that thing. People are interested in impressionism, people are interested in the art market in the 1850s. Getty is not a representation of the art market in 1850s. We just hold part of that picture. We're nonprofits, our job is to hold objects and knowledge and trust for the public, and so we're not in competition with people like The Met. They're friends, they're peers, they're helping us in this larger project. David: And so Cultural Heritage is this amazing place to do data work because my job is to give the information away and to give it away in ways that make it easy to connect with other people's information sources. And so I think again, the promise of linked data has not been fully realized, I think often because of the tension between the idea that you connect all the knowledge in the world and the reality that knowledge is commercially valuable and maybe giving it away is not the way to capture that value. But in cultural heritage, we don't have that second part, and so it means we can really play with this stuff in ways that some of our commercial colleagues have a harder time doing. Larry: Oh, that's so interesting that you're unfettered by the commercial part of this whole thing and you can be true because we're celebrating the 25th anniversary of the semantic web this year, and that's one of the ostensible failures is the inability to have a common understanding across the whole thing. But if you're in a non-commercial space and you're just concerned with the facts about a bunch of stuff that you've got,...

François Scharffe As AI unleashes a host of new tools and agents to help build knowledge graphs, engineers like François Scharffe are tasked with harnessing these new capabilities and deploying them effectively. François brings decades of AI experience to the goal he set for himself earlier this year: to automate his own job. He's making good progress, but he's also finding that there's still plenty of need for him and other humans in the workflows he's designing. We talked about: the upcoming 2027 edition of the Knowledge Graph Conference his long history of working with various kinds of AI the distinctions between knowledge engineering and software engineering the rationales behind his choice of Pi as his coding harness an overview of his harness set-up and knowledge engineering workflow the benefits to knowledge graph building that AI brings the variety of tools and agents he has built for tasks like requirements specification, data sourcing, ontology design, term extraction and re-use, entity resolution, data mappings, ETL pipelines, etc. the evaluation processes he's using to assess the benefits of his new workflows the ongoing need for humans in the loop, particularly for tasks like ontology creation how he has adapted his work style for the AI era, in particular the number of tasks he now does in parallel with AI how he manages ongoing improvements to his system - tracking decision traces and implementing introspection steps as well as devising scenarios and using "critic" agents to improve processes the reason he downplays prompt writing and review in his operation in favor of system evaluations his scientific approach to system evaluation, aiming for reproducibility when moving from one model to another the focus on agent memory in the 2027 Knowledge Graph Conference program François' bio Francois Scharffe is a hands-on enterprise knowledge architect and semantic AI leader. He has built and stewarded production knowledge graphs across several domains, designed ontology, taxonomy, metadata, entity resolution and data integration capabilities, and connected structured knowledge systems with AI and agentic workflows. His career spans enterprise engineering, data and AI strategy consulting, product leadership, research, and community building. François is also a founder of the Knowledge Graph Conference, a leading forum for knowledge-centric AI technologies. Connect with François online LinkedIn email: francois at knowledgegraph dot tech Resources mentioned in the interview Pi coding harness the original xkcd "compiling" cartoon the cartoon updated for the AI era Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 58. AI unleashes a whole host of new tools and agents to help engineers build knowledge graphs. Harnessing these new capabilities and deploying them effectively is the job of the modern knowledge engineer. François Scharffe brings decades of AI experience to the goal he set for himself earlier this year: to automate his own job. He's making good progress, but he's also finding that there's still plenty of need for him and other humans in the workflows he's designing. Interview transcript Larry: All right. Hi, everyone. Welcome to episode number 58 of the Knowledge Graph Insights podcast. I'm really excited to welcome back François Scharffe. He was in the first episode of the podcast series a couple of years ago with his colleague Tom Deely. They run together the Knowledge Graph Conference. But François is also a practicing in-the-trenches knowledge engineer in New York City, and we're going to talk today about some cool workflow and architectural stuff he's developed. But welcome, François. Tell the folks a little bit more about what you're up to these days. François: Hey, Larry. Great to be with you today. What am I up to today? A bunch of things. I mean, the number one thing is the Knowledge Graph Conference, which we're preparing for May in New York. So we're moving from Cornell Tech to Manhattan, Central Manhattan, Bryant Park. This is exciting, larger venues, more rooms, larger rooms. And we're going to open the program, the call for presentation soon. So if you're interested to discuss knowledge graphs, you have interesting projects to discuss, then yeah, keep an eye open for the call that's going to come out soon. François: Yeah, but I think you invited me to talk more about my other activities around knowledge graphs. As you say, I'm a knowledge engineer. I started my career in academia, and I've always come back to it at some point, either teaching or doing research. But now for several years I've been engineering, so I've been a knowledge engineer, in the weeds of the practice, and recently got pretty excited to see how AI is changing the job. So happy to discuss that if you're interested as well. Larry: Yeah, no. We had a quick chat. I was in New York last week, and we had a quick chat about it, and I just got unreasonably excited about what you're up to because everybody's wrestling with this stuff now, like how to incorporate LLM, generative AI and other AI tech into their engineering workflows of all kinds. But specifically for this audience, for a knowledge engineer to be doing the stuff that you're doing... Larry: Well, I guess, first tell me a little bit like... I guess like any software engineer these days, you feel obligated to use these tools. And then you have to harness them in some way, and that's led to this new term, I think arose within the last year, of the harness. And you're working on a very specific kind of harness specifically for knowledge engineers. Can you talk a little bit what the impetus was for that, why you decided to approach your work this way, and then maybe just a quick overview of how it works? François: Yeah. Yeah, yeah. So, I mean, the first thing I want to say is I'm a knowledge engineer, but also a background in general machine learning and data science, natural language processing. And I've been following the evolution of neural networks to language models and large language models for years, being more or less involved practically with them, but close to the developments there. And remember GPT-2 coming out and the poems it was writing about unicorns and finding that so amazing. But how long ago was it? I can't even remember. 2018, 2019. Larry: I was going to say that's about eight or 10 years. I remember. François: Right. Larry: Yeah. François: So when there started to be a big excitement about AI, I was originally quite skeptical, still following but not really being so enthusiastic as to its possibilities to do everything, especially in my field, knowledge engineering, that... We can come back on the details of it, but it gets pretty complex in terms of the sort of workflows that are involved. But yeah, last December there was this realization that... I think it was Claude 4.8 or GPT 4.8. Sorry. Larry: I think it was Claude. If I remember right, all my engineer friends got excited about Claude, I think is the one that really like, "Okay, here we go." François: Right. I think it was Claude. Yeah. And I was like, "All right. I think something has changed now. We passed a capability level, and let's go and jump to that." And so now being a knowledge engineer and being a software engineer, those are separate jobs. We're pretty clear about it. And I think the models and the software coding agents, like Claude Code or Codex or others, they have large prompts that makes them specialize for coding, so for coding tasks, for code reviews, for code basis maintenance, et cetera, et cetera. And that told me that those were not necessarily the best tools for building an AI that does knowledge engineering. François: Because one reason is that this will take a lot of context space with specific instructions for coding that are not necessarily relevant for knowledge engineering. Although there is coding involved in knowledge engineering, it may not be as complex. You will write scripts or some programs, but it will be always in a part of a certain workflow in a repeated and self-contained way compared to general software engineering. So that was one thing. François: And then another was we need specific tools and specific workflows for knowledge engineering. But also, I think there was part of me that likes open source and building my own things and adopting Claude Code was for me like, "This is something we have no control over. We don't even know what's in it." Now we know a little bit better, but at the time. François: So I was looking around and found this minimal coding harness called Pi. And there are others. There's OpenCode, but I went for Pi. It's very minimal and extensible and made for being extensible, for being extended. And so I went there and adopted it and was like, "Hey, I'm now an experienced professional, and I've been doing that job for years, and I have a lot of knowledge on what a knowledge engineer is. Why don't I try to build an artificial knowledge engineer to automate my job?" And the New York came, and the New York... I woke up, and I was like, "All right. This year, I'm going to automate my job. I'm going to make myself useless." So yeah, that was the goal with it. François: And yeah, harness, to go back to your question... So a harness is a minimal environment that sort of helps contain your wild LLM, your wide AI models. If you just talk to that model, interact directly with the model and asks it to do some knowledge engineering task, it will go left, it will go right, it will go where it decides to go like a wild animal, a wild horse, but not necessarily bring you from A to B in a straight line. Eventually you may reach your point, your destination,...

Jessica Talisman When the idea of the semantic spectrum was introduced 25 years ago, it was meant to be a conceptual tool to help programmers and architects understand the relative benefits of different levels of semantic effort. Now, a couple of decades later, Jessica Talisman has repurposed the model as a way to organize the actual process of developing enterprise knowledge infrastructure, which she shares in her new book, "Ontology Pipeline: A Framework for Knowledge Engineering." We talked about: her new book, "Ontology Pipeline: A Framework for Knowledge Engineering" the inspiration she took from Obrst's original semantic spectrum her language-first approach to ontology her pre-RDF semantic work with standards like Dublin Core the three "warrants" from library science that guide functional requirements specification the origins of her book in a 2018 Department of Justice project and its conceptual development in subsequent projects how the ontology pipeline emerged from her process of incrementally delivering artifacts in projects the importance of teasing out and addressing assumptions that can derail ontology projects the crucial need to meet an organization where its at in terms of its current semantic capabilities how her education-first approach helps get enterprise buy-in for ontology projects how using RDF in vocabulary and taxonomy work paves the way to broader ontology work the power of working with concept graphs, in particular its benefits in AI architectures the benefits of symbolic representations of concepts like attribution and provenance in AI systems the dual nature of her book as both an instruction manual and an ongoing reference resource Jessica's bio Jessica Talisman is a Semantic Engineer and Information Architect with more than 25 years of experience building knowledge infrastructure across enterprise architecture, e-commerce content systems, digital libraries, and knowledge management. She has led semantic architecture initiatives at Adobe, Amazon, Overstock, Pluralsight, Senzing, Vanguard, the Department of Justice and client engagements spanning the Smithsonian and the USC Shoah Foundation. Jessica is the creator of the Ontology Pipeline™ — a framework that moves from controlled vocabularies through taxonomies, thesauri, and ontologies to fully realized knowledge graphs — and founder of Contextually LLC, her consultancy in ontology modeling, NLP integration, and knowledge graph design. She also works with The Knowledge Graph Academy, a cohort-based program training the next generation of ontologists and semantic engineers. Jessica publishes the Substack newsletter Intentional Arrangement. Her book, Ontology Pipeline: A Framework for Building Knowledge Infrastructures(Technics Publications), arrives September 2026. Connect with Jessica online LinkedIn Intentional Arrangement The Knowledge Graph Academy Intentional Arrangement SKOS tool Order Jessica's book Ontology Pipeline: A Framework for Knowledge Engineering Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 57. When the idea of the semantic spectrum was introduced 25 years ago, it was meant to be a conceptual tool to help programmers and architects understand the relative benefits of different levels of semantic effort. A couple of decades later, Jessica Talisman repurposed the model as a way to organize the actual process of developing enterprise knowledge infrastructure, which she shares in her new book, "Ontology Pipeline: A Framework for Knowledge Engineering." Interview transcript Larry: Hi everyone. Welcome to episode number 57 of the Knowledge Graph Insights Podcast. I am super extra delighted today to welcome to the show Jessica Talisman. Jessica, if you've been living under a rock, just in case, I will introduce her as the person. She's the owner and founder of Contextually, a knowledge graph consultancy. She's an instructor and a curriculum designer with the Knowledge Graph Academy with Tony Seale and Veronica Heimsbakk and Katariina Kari. She's also the author — and this is what we're going to talk about today — of the forthcoming book, Ontology Pipeline: A Framework for Knowledge Engineering. So welcome, Jessica. Tell the folks a little bit more about what you're up to these days. Jessica: Thank you. So yes, I am working as a consultant and so I'm working with clients and customers. Also, to Larry's point, teaching and developing new curriculum and new course options for Knowledge Graph Academy soon to be released. And then my book, Ontology Pipeline, is based off of a framework I developed that was a riff on the old semantic spectrum, but is a composable and decomposable way to build knowledge infrastructures. And the book will be released and available somewhere around the September 16th and 17th. Larry: Excellent. I'll probably drop this on either the August 31st or September 1st. So folks will have a couple of weeks. I'll put a link to the book in there. Well, that's interesting. I didn't know that this was originally a riff on the semantic spectrum. Are you talking about Obrst's original semantic spectrum and then the subsequent. Yeah, talk a little bit about that. Jessica: Yeah. So I felt like it was an abstraction at the time of where semantics lived, but those of us within the semantic and knowledge engineering space have always acknowledged that semantics does exist on the spectrum. And when I say semantic, I mean context and meaning type of semantics where we introduce ontologies and taxonomies and these information and knowledge artifacts. But they are varying ... You can think of it as a spectrum of maturing structures to be able to go from something where you're just simply giving context and meaning all the way to introducing formal logic into a system. So that's where we start with controlled vocabularies. That's step one. The second step is metadata schemas, which is a super important step that's most overlooked, I think. Then taxonomies, then thesauri, then ontologies and then knowledge graphs. So it's a framework that brings you through the iterative states of building a knowledge infrastructure. Larry: Yeah. That's so funny. I knew that it paralleled that, but I hadn't thought about that was the riff on it. Because the thing I really appreciate about your work is that you come out of this ancient tradition of library science and well, duh, you're going to take a language-first approach. And so vocabularies make a sensible starting point from that way as well. Was that just ... Not serendipity, but just like, well, duh, that's just the way the world is. You start with vocabulary and build from there. Jessica: Yeah. Well, and I think it really came out of working. I started my career back in the late 1990s and it was right at that precipice when RDF was being introduced and I was working on this huge Steven Spielberg project, largest visual history catalog ever. To make two to six minute segments of this video available to end users. And we had more than 11 and a half years of footage back to back. So if you imagine this huge catalog, how do you make these little clips? It was well before YouTube and clips. Discoverable. So it was very, very early proof of concept and that at the time was based entirely on language because we had to tag content, we had to have consistency. Jessica: So we essentially built a thesaurus library or librarian style that really emerged out of the library sciences and cataloging practices and records and those sorts of things. You can think of it as the golden records. But the idea was having this consistency because where do we define meaning as humans? And that's what it really was working towards. And thinking about how do I present something that is a repeatable process within an organization that logically makes sense that starts with humans, which is how we define things. Because ultimately when we build a knowledge infrastructure, it's about how humans define this meaning, the first step is not how machines define meaning. Larry: Yeah. There's so much in there. There's five podcasts just in what you just said, but I'm really curious about when you said defining meaning in that project. 30 years ago, you don't have RDF, you don't have SKOS, you have- Jessica: We have Dublin Core. We had Dublin Core. Larry: Oh, right. So you have your library tooling. Jessica: I had the library. Larry: Yeah. Tell me about, I guess maybe a quick contrast between how you did it then, just quick overview of how you use Dublin Core for the ... And I assume that was key to your metadata strategy and everything that drove that. And then how would that unfold today? Jessica: Oh gosh. And you know what? Actually, that's a really good point. I would say that a lot of the practices and a lot of the core tenants of those types of systems ... So using Dublin Core, which has a more mature standard for data catalogs, which is called DCAT, and we also have DPROD, which is for data products. There are these really cool W3C standards that have evolved out of the library sciences that are based on Dublin Core. Dublin Core is not ... It's so elegantly designed because the Dublin Core format first started as 15 core elements, and those core elements were like title, description, author, all of the basic metadata that you would want to apply to things with digital ecosystem. So it was that formative ... It doesn't declare anything as a class or a property. The original thing was you just had 15 elements that could be used to describe documentation in a digital space. So that one ... And I think of it as a seed, that one seed emerged into this whole family of standards that are used to describe things in digital ecosystems. Jessica: So for example,...

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....

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,...

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

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

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....

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,...

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,...