LLMs, data-driven AI, and renewed role for knowledge

Jim Hendler As the World Wide Web emerged in the late 1990s, AI experts like Jim Hendler spotted an opportunity to imbue in the new medium, in a scale-able way, knowledge about the information on the web along with its simple representation as content. With his colleagues Tim Berners-Lee, the inventor of the web, and Ora Lasilla, an early expert on AI agents, Jim set out their vision in the famous "Semantic Web" article for the May 2001 issue of Scientific American magazine. Since then, semantic web implementations have blossomed, deployed in virtually every large enterprise on the planet and adding meaning to the web by appearing in the majority of pages on the internet. We talked about: his academic and administrative history at the University of Maryland, Rensselaer Polytechnic Institute, and DARPA the origins of his assertion that "a little semantics goes a long way" his early thinking on the role of memory in AI and its connections to knowledge representation and to SHOE, the first semantic web language his goal to scale up knowledge representation in his work as a grant administrator at DARPA how different departments in the US Air Force used different language to describe airplanes the origins and development of his relationship with Tim Berners-Lee and how his use of URLs in SHOE caused it to click how he and Berners-Lee brought Ora Lassila into the semantic web article how his and Berners-Lee's shared interest in scale contributed to the "a little semantics goes a long way" idea why he lives in awe of Tim Berners-Lee Berners-Lee's insight that a scaleable web needed the 404 error code how including an inverse functionality property like in a relational database would have ruined the semantic web how they came to open the Scientific American paper with an anecdote about agents his early involvement in the AI agent community along with Ora Lassila their shared conviction of the foundational importance of interoperability in their conception of the semantic web how the lack of interoperability between big internet players now is part of the reason for the inability to fully execute on the agent version they set out in the SciAm article the impact of LLMs on the semantic web early examples of semantic web linked data interoperability Google's reclamation of the term "knowledge graph" the reason that the shape of the semantic web was always in their mind a graph how the growth of enterprise data led to their adoption of semantic web technology how the answer to so many modern AI questions is, "knowledge" Jim's bio James Hendler is the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI where he also serves as a special academic advisor to the Provost and the Head of the Cognitive Science Department. He also serves as a member of the Board, and former chair of the UK’s charitable Web Science Trust. Hendler is a long-time researcher in the widespread use of experimental AI techniques including semantics on the Web, scientific data integration, and data policy in government. One of the originators of the Semantic Web, he has authored over 500 books, technical papers, and articles in the areas of Open Data, the Semantic Web, AI, and data policy and governance. He is the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. In 2010, Hendler was selected as an “Internet Web Expert” by the US government, helping in the development and launch of the US data.gov open data website and from 2015 to 2024 served as an advisor to DHS and DoE board. From 2021-2024 he served as chair of the ACM’s global Technology Policy Council. Hendler is a Fellow of the AAAI, AAIA, AAAS, ACM, BCS, IEEE and the US National Academy of Public Administration. In 2025, Hendler was awarded the Feigenbaum Prize by the Association for the Advancement of Artificial Intelligence, recognizing a “sustained record of high-impact seminal contributions to experimental AI research.” Connect with Jim online RPI faculty page People and resources mentioned in this interview Tim Berners-Lee Ora Lassila Deb McGuinness The Semantic Web, Scientific American, May 2001 Introducing the Knowledge Graph: things, not strings Massively Parallel Artificial Intelligence paper Attention Is all You Need paper Vision conference Is There An Agent in Your Future? article "And then a miracle occurs" cartoon Jim's SHOE (simple HTML ontology extensions) t-shirt Video Here’s the video version of our conversation: https://youtu.be/DpQki6Y0zx0 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 43. Twenty-five years ago, as AI experts like Jim Hendler navigated the new World Wide Web, they saw an opportunity to imbue in the medium, in a scale-able way, more knowledge than was included in the text on web pages. Jim combined forces with the web's inventor, Tim Berners-Lee, and their mutual friend Ora Lasilla, an expert on AI agents, to set out their vision in the now-famous "Semantic Web" article for Scientific American magazine. The rest, as they say, is history. Interview transcript Larry: Hi everyone. Welcome to episode number 43 of the Knowledge Graph Insights Podcast. I am super extra delighted today to welcome to the show, Jim Hendler. Jim, I think it's fair to say he literally needs no introduction. He was one of the co-authors of the original Semantic Web article in Scientific American. He's been a longtime well-known professor at Rensselaer Polytechnic Institute. So welcome, Jim. Tell the folks a little bit more about what you're up to these days. Jim: Sure. Just to go back a little further in history, I've been doing AI a long time and my first paper was about '77, but a lot of the work we're going to be talking today happened when I was a professor at the University of Maryland, which was from '86 to 2007. And then from 2007 on, I've been at RPI where I was really hired to create a lab that really would be a visionary lab on semantic web and related technologies. I think the president of the university saw the data science revolution coming and saw that that was a key part of it. Jim: So who am I? What am I? Really, what happened was very early in the days of AI, I was working in a lot of different things. I started under Roger Schank at Yale, took a few years off to work professionally at Texas Instruments, which had the first industrial AI lab outside of the well-known ones at Xerox Park and stuff. Then decided no, I really was an academic at heart. So I came back, went to grad school with Gene Charniak at Brown and went from there to the University of Maryland. So you know my job life history. I've bumped around during that time. Living in Maryland, you tend to bump into the Defense Department and things like that and funding and things like that. I was on a few committees and things like that. Eventually asked to come to DARPA for a few years, which is really where a lot of our conversation today probably starts. Jim: And then again, just because it was successful and we had a visionary president here at RPI, she asked me to come and said, "Not only do I want to hire you, but I want you to hire a couple other people you'll work with who'll help put us on the map and this stuff." And I hired Deb McGuinness and I'm sure that'll come up later. And then past 15 years have been a combination of research and administration. So I've done both, doing my own work, working with my students, and also trying to really set up some significant presence of AI on our campus, AI and beyond. Larry: Nice. Yeah, and we'll talk definitely more about your research work and everything. But hey, I want to set a little bit of context about how we met, because I know Dean Allemang from the Knowledge Graph Conference community, and we'll talk a little bit more about the book that you wrote with him later on. But one of the things that he famously says, and always attributes it to you, is that phrase "A little semantics goes a long way." I'd love to open up by talking a little bit about that. Jim: So early on in AI, it was becoming very, very clear to me, and now I'm talking 70s, early 80s, so a long time before we were where scaling means what it does today. But it's very clear to me that a lot of the problem with AI is it didn't scale. And meanwhile, I was seeing these other technologies coming along, the ones that really led to the web, that were looking at a much, much broader thing than the typical AI system. So one of the things I started asking is, how do we scale up AI? And we were looking at traditional knowledge representation languages. I actually have a paper from the 80s. I actually did a book with Hiroki Katano, who's now the... I believe he's still the vice president for research at Sony, if not something higher. And Katanosan and I actually had a book called Massively Parallel Artificial Intelligence in the 80s, but it became clear to me that the machines were part of the story, but the lots and lots of people doing lots and lots of different things was the much more interesting part of the story. Jim: And then also, I've always been intrigued by human memory. You asked me a question and I not only answered that question, but I'm doing right now. It's associating a million things in my mind. And what I'm really doing is winnowing rather than trying to come up with the precise answer. And so I started thinking about how does AI memory start to look like human memory more? In those days, a thousand and then 10,000 and then a million "axioms" were very, very large things, and that's what I wanted to do. And then the web was coming along and I saw that, well, if I'm going to get a million facts about something,