Eric Little: Super Domains, the Original Context Graph – Episode 56

Eric Little Knowledge graphs are complicated. They might entail multiple ontologies, several data sources, and any number of generated graphs and other context-establishing elements. Eric Little came up with the idea of "super domains" to understand and manage these important but ephemeral pieces of an enterprise's semantic knowledge architecture. A super domain might establish context that holds for just a few seconds for an ATM transaction or for several years for a clinical trial. We talked about: his recent move from Accenture to Knowledge3 as Chief Data Officer his diverse background in the consulting and enterprise worlds super domains, the conceptual framework he has developed to deal with generated graphs, reification, and other knowledge graph elements how super domains capture context some examples of super domains how super domains can help you deal with temporary, ephemeral, and stochastic data how his philosophy background led to his early exploration of the concept of context graphs the crucial distinction that needs to be made between epistemology and ontology in semantic practice the role of phenomenology in his conception and practice of ontology his take on the current state of semantic technology practice how generative AI has brought the importance of metadata to the fore and highlights the need for deterministic capabilities in AI architectures the dearth of tooling in the semantic space and the need to deliver "semantics at the speed of AI" Eric's bio Eric Little, PhD is Chief Data Officer at Knowledge3. He previously was Industry Innovation Principal Director & Head of Semantic Strategy & AI for Global Assets at Accenture. He received a dual Ph.D. in Philosophy and Cognitive Science in 2002 from the University at Buffalo, State University of New York. His Post-Doctoral Fellowship at the University at Buffalo’s Department of Industrial Engineering (2002-2004) focused on developing ontologies for multisource information fusion applications. He has worked in academia as a professor in several fields at several universities, as well as held multiple management & C-level positions in the software development industry across several different industry verticals. Having such a diverse background spanning academia & industry over the years provides Eric with a very unique set of experiences in the software development space that cuts across numerous disciplines and business verticals. After receiving his PhD and subsequently doing his post-doc, Eric has held various academic positions including Assistant Professor of Doctoral Studies in Health Education & Health Policy and founder of The Center for Ontology & Interdisciplinary Studies at D’Youville College. During this time he also started and ran his own consulting company which landed several high-profile customers across industries, including healthcare, insurance, oil & gas, and medtech. He is a world-recognized expert in semantic technologies, data fusion applications, data modeling, analytics, and AI. He has numerous professional publications in these areas, has been featured in industry publications, and is a well-known speaker at conferences around the globe. Before working at Accenture, Eric co-founded and was CEO of LeapAnalysis, the world’s first fully virtualized semantic search & analytics data science engine, which was named the #3 Most Innovative Data Science Company In The World by Fast Company Magazine in early 2021. He also was named Most Innovative CEO by Global CEO Magazine. He brings this knowledge and his passion for innovation everywhere he goes, developing new technologies and furthering the growth and success of his client base. Eric has a very simple goal in life – just change the world by making things no one has seen or thought about before. Eric is married and lives on the barrier island in Indialantic FL with his wife Jodi and their 2 cane corsos, Lemmy & Eddie. His daughters Gabrielle & Claudia are recent grads from University of WI Law School & Florida State University School of Business, respectively. For his personal life, Eric is a former semi-pro musician and is an avid guitar player & collector. He currently has a small digital studio at his house where he still writes and records songs (fun fact: his previous band, Satori, from the late 80’s-early 90’s is listed in The Encyclopaedia Metallum). He is also a motorcycle enthusiast and likes to tinker on his vintage bikes when time allows. Connect with Eric online LinkedIn Knowledge3 Photo of "super domains" slide in Eric Little's 2026 KGC presentation Video Here’s the video version of our conversation: Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 56. A knowledge graph is a complicated thing. It might entail multiple ontologies, several data sources, and any number of generated graphs and other context-establishing elements. Eric Little came up with the idea of "super domains" to understand and manage these important but ephemeral pieces of an enterprise's semantic knowledge architecture — context that might hold for as little as a few seconds for an ATM transaction or for several years for a clinical trial. Interview transcript Larry: Hi, everyone. Welcome to episode number 56 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, Eric Little. Eric has most recently become the Chief Data Officer at Knowledge3. And welcome, Eric. Tell the folks a little bit more about what you're up to and your transition into this new role. Eric: Yeah, thanks, Larry. It's been a wild ride. It's been about a month now. Yeah, so I left my position at Accenture where I was leading a lot of our strategy practice around semantics for AI. But I happened to get back in touch with an old colleague of mine, Tom Plasterer, who I'm sure you and everybody knows. So Tom and I have had a long relationship. I mean, ironically enough, it's kind of funny, we're both actually from Wisconsin. Larry: Oh. Eric: So we're both Packers fans. He was born in Madison and grew up there. I was born in Green Bay and grew up there. So we come from kind of a similar Midwest background. We both root for the same football team. We both got into semantic scientologies. We've both worked a bunch in pharmaceuticals and life sciences, so it's kind of strange. We always refer to each other as sort of brothers from another mother kind of thing. And so Tom approached me about the company and it was a really great conversation and a good idea. Eric: And it really got me thinking because it was very similar to a company I had co-founded and was CEO of before called LeapAnalysis, which was a data virtualization company where we made Fast Company's number three most innovative data science company in the world in something like 2021, I think. But I went to Accenture. I've been there for about the last five years. Accenture was great, really good company to work for. Eric: Massive company as you know. And so now I've gone from something that's almost 800,000 people back to something that's like six people. So a little bit getting my sea legs there, but it's been really great. I sort of jumped in, started working with some of our clients already, and we're pushing the envelope on some of this stuff that we're doing in semantics, especially around things like virtualization, federation, and really serving up a semantic backbone for things like AI. Larry: Very cool. It's like what everybody's kind of doing now, stuff in that area. There's so much demand for this. It's really gratifying to see it. But I'm really curious about for you as a practicing ontologist and consultant, you were doing, as I recall, you were doing more kind of standard, not standard, but industry stuff and more like... Whereas knowing Tom and you and K3, are you focused on pharma and the life sciences in this new practice? Eric: We are currently, and a lot of that is because we're small and we want to be focused. So we both have a really strong background. Tom, probably stronger than mine even in this, as well as our CTO, Ivan, Ivan Stankov is another guy who comes from the AstraZeneca background. So those guys have been really, really deep into pharma. I had been a consultant in pharmaceuticals. I've been a consultant in medical device. I've also though been a consultant in everything like oil and gas, finance, consumer packaged goods. Eric: I spent a lot of my career in the defense industry. So I used to be one of these guys with a top secret SCI clearance doing a lot of three-letter agency work and stuff like that, building ontologies around threats and stuff for data fusion applications. But Tom and I had this talk and we decided, "Hey, look, we both have a pretty good Rolodex in the space. It's a good place for us to get started. We have a lot of deep subject matter expertise and knowledge in the space, so it makes a lot of sense." Eric: We're also both really, really into fair data, making data both findable, accessible, interoperable, and reusable. And those standards are really prevalent in the life sciences space. I mean, if you look around life sciences, there's something like 1,100 plus ontologies to grab in the life sciences space, whereas you go to finance and you have basically FIBO, and you go to something like supply chain and you've got one or two. Right? And you've always just got a couple of papers on things. Eric: But so I think it's sort of by design, but also through a product of necessity. But that doesn't mean we need to stay there. We do have desires to move eventually as we grow out of life sciences. But Knowledge3, as a technology, it's not bound to the life sciences at all. I mean, we could apply this to virtually anything, but you do need good heavyweight semantics when you are doing this kind of stuff in life sciences....