Ben Rode: Porting the Cyc Ontology to RDF – Episode 53

Ben Rode As the Cyc project winds down, one of its main contributors, Benjamin Rode, is attempting to migrate as much as possible of its upper ontology to RDF. It's an ambitious project, much like Cyc itself. Cyc arose from the early AI research community, and Ben has a fascinating perspective on that milieu as well. We talked about: his work to port as much as possible of the Cyc upper ontology into RDF the surprisingly long history of neuro-symbolic AI some interesting details and anecdotes about the origins of the field of AI the deep intertwining of the histories of symbolic AI and computing itself Doug Lenat and the origins of the Cyc project and the Cycorp company Lenat's concept of "white space knowledge," the framing for unstructured natural language text how an attempt to port the Cyc ontology to RDF is "not as insane an exercise as it might sound at first" some parallels and distinction between the RDF and Cyc worlds the unique characteristics and capabilities of the CycL language, in particular its homoiconicity the central question for the current era: "can these two frameworks (symbolic AI and probabIlistic AI) work together in a synergistic way" Ben's bio Ben Rode came to formal domain modeling and ontology engineering by way of Douglas Hofstadter’s Gödel, Escher, Bach: The Eternal Golden Braid, which he read in high school. His interest has since developed into study of machine learning, causal inference and induction, temporal reasoning, ontology evolution, and neurosymbolics. He holds a graduate degree in philosophy with philosophy of mind and analytic philosophy as areas-of-focus; the subject of his dissertation was the use of formalized contexts in common sense reasoning. He joined the technical staff at Cycorp in 1997, where he's played an active role in developing the Cyc ontology for a number of contracts, including extensive work on database schema integration, ontology extension and mapping, inference development, and domain knowledge acquisition from subject matter experts, in addition to assisting with research on using the Cyc ontology for LLM-assisted formal knowledge capture. His current research interests include translating a subset of the upper Cyc ontology into RDF, large language model-assisted knowledge graph extension, and the use of knowledge bases for validation and verification of large language model output. Connect with Ben online LinkedIn email: benjamin dot paul dot rode at gmail.com Resources mentioned in this interview Cyc Automated Mathematician Heuretics: Theoretical and Experimental Study of Heuristic Rules Eurisko Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 53. It turns out that contemporary explorations of hybrid AI are actually re-opening conversations that started almost 70 years ago. In the early days of AI, neural network "connectionists" and symbolic AI researchers saw their work as naturally complementary. Out of that primordial AI ecosystem emerged Doug Lenat's Cyc project, an ambitious effort to account for all of humanity's common-sense knowledge. Ben Rode is now trying to bring that work to the RDF world. Interview transcript Larry: Hi, everyone. Welcome to episode number 53 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, Ben Rode. Ben is a longtime ontologist, best known for his association with the Cycorp project, and we'll talk a little bit about that and the Cyc Project in general. He's also working on, and the reason I wanted to have him on the podcast today, he's working on a project to port the Cyc attempt to account for common sense knowledge in the world into the RDF world that most of us are familiar with. So welcome, Ben. Tell the folks a little bit more about what you're up to these days. Ben: Okay. So in the course of finding my way to a new position, I'm working on several projects of my own. One of them, as you mentioned, involves trying to port as much as can be ported from the upper Cyc ontology, particularly the 2014 version of that that is in the public domain. It's available under public licensing on GitHub, to try to port as much of that as possible into the Resource Description Framework and the semantic web. And this has actually become an experimental interest of mine and I think it may be we can learn quite a lot from doing that. Larry: Yeah, that's a good opportunity to remind me to ask you about the history of all this. And it goes back, you did a presentation a while back that I saw, where you set out the overall history of the development of the field of AI and how Cyc and other things fit into it. Can you talk a little bit about that? Ben: Yeah. Well, I mean, I think the framing context there is partly the question of, well, why would you want to translate the upper ontology of Cyc into RDF and what would that be good for? And perhaps there's even a larger question there of what would Cyc or the semantic web be good for in the current AI context? Now, I mean, I think the story here really is maybe much older than a lot of people realize. I mean, the roots of AI generally go back quite far. And here I think it's very important to distinguish between what we sometimes hear referred to as symbolic AI versus sub-symbolic. I think both the semantic web technology stack and Cyc can fairly be described as symbolic AI. Large language models are a good example of sub-symbolic AI, sometimes referred to as generative or probabilistic AI. Now, I mean, it's really interesting that this really starts... For symbolic AI, it's really quite early. I mean, it starts back at the end of the 19th century really with what we might term as math validation. Ben: What you have here is a set of concerns coming up in the mathematical community that the foundations of mathematics might not in fact be sound, that there might be hidden contradictions. And certain developments, spearheaded, for example, by Bertrand Russell, it led people to believe that the worst fears might be realized. So what you have developing in the late 19th and early 20th centuries is an effort to put mathematics on a sound foundation. And one of the things that comes out of this, not only is it symbolic AI, computing itself and digital computers and programming really have their origins within this thread. And then a little later than that, really starting up maybe in the early 1940s, you have information theory, which is coming with Claude Shannon and then associated with that is a movement that has been almost completely forgotten now and I think regrettably forgotten. Ben: It was called Cybernetics. If I remember right, the name originated with the Greek, hopefully I'm pronouncing this right, kybernētes, meaning steersman. I mean, the interest was in homeostatic systems, our systems where there was re-entrancy of information and energetic cycling to maintain homeostatic conditions. And there was a belief that this could tell us a lot of things, physics, biology, information, science. Some of the key names there are Norbert Wiener, Heinz von Foerster. Let's see, I think it was, I believe Humberto Maturana and Francisco Varela and also a guy named Hans Jonas, who I've only recently found out about, and I'm starting to believe, beat everyone to the punch on that. But this is the thread that in some ways gives rise to generative AI. I mean, it comes through things that were called perceptrons that were pioneered by Warren McCullough, his student Walter Pitts. Ben: But an interesting aspect of it is that when you go back and look at some of these writings, particularly Norbert Wiener and Claude Shannon, one of the things that really comes up is they were talking in terms of complementarity. They were talking in terms of what we would call symbolic and sub-symbolic AI complimenting each other. Shannon was talking, for example, about auto-tuning versus code optimization. And if you think of auto-tuning roughly as being the machine learning, generative AI end and code optimization being the symbolic end, those were seen as being mutually supportive. And I think that is a feature we really want to... We're at a point where we need to be reexamining that. I mean, it is being reexamined under the heading of very... We hear about hybrid systems or neurosymbolic AI and this really is it. We can talk some about what are the comparative strengths and weaknesses of symbolic and sub-symbolic AI in a little more detail if you like, but I think we- Larry: I love that too, that everything you just said goes back, because virtually everybody else I've talked to kind of marks that 1956 Dartmouth Conference as the dawn of AI. Well, that's where McCarthy coined the term AI. Ben: That's where McCarthy coined the name, yes. Larry: Yeah. But also it just occurred to me like, duh, that didn't just come out of nowhere. And in fact, I talked to our mutual acquaintance, Pat Hayes, about the origin of that and he said, "Yeah, the whole notion, that coining of the term artificial intelligence was to distinguish it from cybernetics, to distinguish-" Ben: Well, no, exactly. And I mean, McCarthy at the time, 1956, he was sort of the young Turk and Norbert Wiener was the gray beard. Again, there may be people in the audience who know much more about this than I do, but based on the accounts I have read and what I have heard, McCarthy was kind of concerned that if Wiener was in that conference, Cybernetics were going to dominate and he didn't want that to happen. He wanted it to be about symbolic AI. So folks, I mean, not only Wiener but McCullough and Pitts were kind of cut out of that discussion. And in some ways that may have been a little unfortunate. I think the history might've been very different if there had been more interaction and more