Knowledge Graph Insights

Robert Sanderson

Yale University manages huge collections of precious cultural heritage artifacts housed in multiple museums, libraries, and other collections.

Using knowledge graph and ontology engineering design patterns that he has developed over his career, Robert Sanderson helps scholars, researchers, and the general public access information about — and make connections across — millions of unique items in Yale's collections

We talked about:

his work as Senior Director for Digital Cultural Heritage at Yale University
the knowledge graph and ontology engineering design patterns that guide his work
the scope of his work — improving discoverability of Yale's extensive collections of artifacts, facilitating the management of collection information, and even collecting data on physical artifact storage facilities
how their linked data approach lets researchers easily connect information about artifacts and information housed in multiple museums, libraries, and collections
how the growth of LLMs has affected their KG user interfaces
how AI is accelerating their ability to add to their knowledge graph the millions of artifacts in their collections that aren't yet accounted for
the compact nature of their three-billion-triple KG ontology, just 10 classes and 50 relationships
the extensive vocabularies and taxonomies they use
how they handle the need to reconcile the identity of lesser-known people who don't have a Wikipedia page or other authoritative references available
how they balance the competing needs of comprehensiveness and usability as they build their knowledge graph
how knowledge graphs facilitate discoveries that other search tools can't
current opportunities for post-docs to join his team to work on leading-edge AI projects

Robert's bio
Dr. Robert Sanderson is the Senior Director for Digital Cultural Heritage at Yale University, where he works with the libraries, archives, and museums to ensure that data and other digital efforts are coherent and connected. He is the principal architect for Yale’s cross-collection discovery system, LUX, which is built on the Linked Art specifications, for which he is an editor. He is also an editor for the IIIF specifications, was the co-chair and editor for JSON-LD and the Web Annotation data model in the W3C. He has previously worked at the Getty in Los Angeles, Stanford University, Los Alamos National Laboratory, and the University of Liverpool. His current areas of work and research are at the intersections of cultural heritage, knowledge graphs, data usability, and generative AI.
Connect with Rob online

LinkedIn
email: robert dot sanderson at yale dot edu

Rob's LinkedIn post series on KG and ontology design patterns

The 10 Design Principles to Live By
Ontology Design Patterns
Naming Things
Avoiding Reification
Foundational Ontologies
Multiple Inheritance, Not Multiple Instantiation
Predicate Reuse... Meh
Document your ABCs
Separate Query and Description Semantics
Usable vs Complete
acknowledgements

Video
Here’s the video version of our conversation:

https://youtu.be/SMAVyrL3aSU
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 46. When your job is to help scholars and the public discover information about millions of cultural heritage artifacts that are housed in multiple museums, libraries, and other collections, you need a powerful — but also manageable — knowledge graph. That's Rob Sanderson's role at Yale University. He and his team apply time-tested ontology and knowledge engineering design patterns to help people discover — and see the connections between — these precious human artifacts.
Interview transcript
Larry:
Hi everyone. Welcome to episode number 46 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Robert Sanderson. Rob is a professor and the senior director of Digital Cultural Heritage at Yale University, the Ivy League School in Connecticut. Welcome to the show, Rob. Tell the folks a little bit more about what you're up to these days.

Rob:
Hi, Larry. Thank you so much for inviting me to be part of the illustrious lineup of guests on your podcast. So yeah, I'm Rob Sanderson, as you said, Senior Director for Digital Cultural Heritage at Yale. So I work with the libraries, the archives, and the museums and other collecting organizations at Yale to help them to be more connected with linked data organizationally and more coherent in the way that we do things digitally. So our projects really focus on discovery and access to the collections in service of the university mission, which of course is teaching and learning, research, and preparing our students to be the next generation of leaders in the world.

Rob:
So for that, the university invests very heavily in the collections, which is fantastic. We are super proud of the 300 years of collecting that we've done. But we want to make sure that if you can't come to New Haven, you still have as good access to those collections as possible. And the ability to find amongst the many millions of objects that we steward exactly what it is that you need. So a lot of our projects focus on describing the collections in a more computationally tractable way so that that discovery can be better. And also how to manage the information that's associated with the collection, but isn't a museum object or a archival object itself. For example, I have two postdocs that are openly available. So if you are a few years out of your PhD or just about to graduate, do get in touch to work on how to use AI to extract the ownership history or the provenance of particular museum objects from the archival content that we also manage. Equally, how can we align research data sets with the collections? So we also have a natural history museum as well as two art museums. How can we align the environmental datasets that are out there on the web with the natural history specimens that could have been impacted by those environments?

Rob:
Yeah. And then equally, we look at the environment of Yale. So we have a large project at the moment to set up environmental monitoring with sensors for light, for humidity, temperature, and so on, to be able to generate a large data warehouse aligned with linked data with the collections so that we can have evidence of what the effects of the environment are on the collection items themselves.

Larry:
Interesting. That is so fascinating. What a fascinating remit. One quick thing about what you just said. Is that about humidity and temperature and all the things that might affect the endurance of these physical artifacts?

Rob:
Yep. Yes. That's right.

Larry:
Yeah.

Rob:
We have about 200 sensors around the place monitoring every five minutes a new data point, which if you think about it, it's actually not that much data.

Larry:
Yeah. I have to say, I just love that you're doing data stuff along with it. That you're not just sitting in a dusty old room collecting things. You're doing cool modern stuff too. But hey, I want to quickly interject how we met, and I just want to put this in because we won't have time to talk about it today, but I want people to know about this fantastic series you did. That's how we met was somebody drew to my attention the series you've done on ontology design and on knowledge engineering design patterns. And I'll point to that in the show notes, but I just wanted to mention. And the more I think about what you just said, because I didn't know all of this background before we started recording, I'm like, "Oh, this is even better than I thought." So I'll point to that in the show notes.

Larry:
But the main thing I wanted to talk about today is what you were just talking about. This amazing cultural heritage operation that you're running there, especially the knowledge graph component of it and the AI, of course, because we're in the 21st century, and that's all anybody talks about. One of the things we talked about before we went on the air was how AI is accelerating the ability for you to build your knowledge graphs of these cultural heritage artifacts and data. Can you talk a little bit about that, how AI is helping in that?

Rob:
Yeah. Of course. Absolutely. So just a little bit of a background about the knowledge graph itself first before I get to the AI part. So over the past five years, we've built without AI, a very large scale knowledge graph, well, in cultural heritage terms of very large scale, which has about three billion triples in it. And it follows the principles and the design patterns that you mentioned in those posts on Linked Art. It then aligns the people, places, concepts, events, objects, works, collections that we manage here at Yale across the two art museums, Natural History Museum, the dozen or so libraries. There's also a collection of musical instruments, the Institute for the Preservation of Cultural Heritage, and we even have a little outpost in London, in England for art history research that we include. So that work uses the linked art ontology, which is based on the foundational site CRM ontology and is publicly available both in terms of the data, you can just download it. But also in terms of the graph queries, we don't force you to learn SPARQL. We have a user interface on top of it, which allows you to generate queries and find the objects that you are looking for.

Rob:
So one of the things that we noticed first about the user interface is that only about 5% of searches are actually using the graph affordances. Mostly, 95% of the time, people just put in keywords because that's what they're used to. You go to Google, you type in your five favorite keywords that you think might match and you scroll through the results. However, now in 2026,

Max Gärber

The promise of agentic AI is being realized in systems like the Service Copilot that Zeiss microscopes provides for its field service engineers.

The system integrates technical documentation, subject matter expertise, and user-generated insights which are orchestrated and shared with a suite of AI agents.

While it relies heavily on modern LLM technology, it's the system's solid knowledge graph and metadata foundation that make it a success.

We talked about:

Max's work "turning information into value" at PANTOPIX, a technical documentation and information processes consultancy based in Germany
a recent client project working with Zeiss to help their field service engineers operate more efficiently
how their prior knowledge management and machine learning work helped them not only cope, but thrive, at the arrival of ChatGPT and LLMs
the immediate positive stakeholder feedback they received as they incorporated LLMs into their knowledge architecture
how they extended the iiRDS standard with a custom ontology and taxonomies and integrated topic mappings into their system and workflows
an overview of the system architecture and tooling, which includes both a graph database and a vector store, an ontology and taxonomy management tool, and documentation of best practices
their evolution from simple prompt engineering and RAG approach to an agentic orchestration architecture
a few of the agents in their architecture:

a planning agent that organizes and orchestrates
a content agent that replaces the original RAG system
a troubleshooting agent which surfaces past solutions


the good problem they experienced of managing enthusiastic user adoption of the new system
the unexpected benefits to the Zeiss sales team of the system
how subject matter expertise, user generated content, and other insights are captured and used
the crucial role of knowledge management practices, structured content, and semantic technology in building the foundation for an organization's AI capabilities

Max's bio
Maximilian Gärber is Partner and Principal Technical Consultant at PANTOPIX. Max has been working in the field of technical communication for over 15 years.

As a Partner and Technical Consultant at PANTOPIX, he is responsible for the technical consultation and implementation of projects. In addition to project management, Max is responsible for data modelling and process optimization in relation to product information (migration, publication, translation) and product catalogues. He is also responsible for product development and ensures that innovative solutions for our customers are continuously developed and optimized.
Connect with Max online

LinkedIn
PANTOPIX

Resources mentioned in this episode

Industrial Knowledge Graph meets Agentic AI: Service Copilot at ZEISS RMS slide deck
Service Copilot from ZEISS article

Video
Here’s the video version of our conversation:

https://www.youtube.com/embed/ttQOHvvxPyw
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 45. When you're a field service engineer dealing with both the typical challenges of information overload and the need to maintain complex machinery like a high-end Zeiss microscope, you'd really benefit from an intelligent knowledge management system, one that integrates technical documentation, subject matter expertise, and user-generated insights. That's exactly what Max Gärber has built - an agentic AI system grounded in a solid knowledge graph foundation.
Interview transcript
Larry:
Hi everyone. Welcome to episode number 45 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Max Garber. Max did a really interesting presentation at the Semantics conference in Vienna last fall, and I've been trying to get him on the show ever since. So here he is. I'm excited to have him here. Max, he's a partner and a technical consultant at PANTOPIX, a consultancy based here in Germany. Welcome, Max. Tell the folks a little bit more about what you're doing these days.

Max:
Yeah, thanks Larry. Thanks for having me. Yeah, great show. And yeah, we are mostly concerned with helping mainly our industrial customers structure their content and integrate it from various sources into their systems, delivery systems, wherever it is needed. So yeah, it's mainly consultancy on data modeling, on how to do information processes and how to get the best out of your data, so to say. So our mission here is literally turning information into value.

Larry:
Oh, I love that. That's a great tagline for a consultancy. Well, you did the use case, the case study you talked about in Vienna was really interesting to me. This issue of Zeiss microscopes, in particular their research microscopy solutions arm, which is these big, expensive, complex machines that require a lot of service. Can you talk a little bit about how you got involved with Zeiss and what you do to help them? In particular, the thing you talked about in Vienna was about the system to help their field service engineers. Can you talk a little bit about that project?

Max:
Yeah, exactly. The main objective there was helping the field service engineer to get the information in that situation when they need it and in the format they need it. That is essentially the bottom line of it. And it started essentially as a knowledge management project. Zeiss, RMS, they have been really into structuring, getting structured content, adding proper metadata to it so it can be used in various cases. The idea has been to integrate from various sources, spare part system, for example, or the manuals from the technical documentation or ticket information and get them into one system so there's a single point of access for the service technicians. So they don't need to spend a lot of time in all of the different systems that there are to get the information about that case they are currently working on because there's a lot they need to consider when servicing or troubleshooting a microscope.

Max:
And yeah, that project evolved into what is now the Service Copilot because I think it was in early '22 when we started the project. And one part of it was to not only integrate all of that information in one place, but also recommend content to the service technician. So, if you were working on a specific case, so the ticket was known, the product was known, you should get a recommendation of articles, "Hey, this is how you install this and that component," for example. So we actually worked a lot on labeling tickets. We actually had a custom labeling interface and used, let's say, classical machine learning approaches to get that recommendations done.

Max:
And it worked not so good, but that was also the same time when GPT, I think it was 3.0 or 3.5 came out. And yeah, we were faced with that situation that there was a new technology available that looked like it could do everything and much more what we were currently doing without much effort. So we really faced the situation there to either stop the project or reinvent ourselves, I would say.

Larry:
I love that juncture. We were talking a little bit before we went on the air about you were really concerned at that point as this arose, but then it turns out that the prior work you had done, the knowledge management work you had done and the machine learning skills and workflows and things you developed, it turns out you ended up being, to my mind, it looks like from that demo I saw in Vienna, at the leading edge of hybrid AI architectures and agentic AI.

Max:
Yeah, I mean, totally. It evolved really quickly. At the point where we looked into GPT and what language models could do, we asked for, "Hey, can we do some quick prototyping research on this and see if we can replace, let's say, the machine learning pipeline that we had with language models?" And it worked really well from the start. So in the beginning, we had 15 service technicians as pilot users that were constantly evaluating the system and giving us feedback, "Hey, that's good, that's not good." And they said immediately, "Well, this is working really well." I mean, they tried, of course, at the very beginning to trick the system and ask the hard questions. And if you look at the content that they are provided, a service manual, it has hundreds of pages and the products that they are servicing, they look quite similar, but they are quite different.

Max:
So there's a lot of variants in what components you can use, how you configure the system, how you buy it. So it's really important that if you have a certain product variant, you don't mix that up. And if you look at how the content is, it is very similar. So of course they have the same structure or a very similar structure and certain, let's say, chapters or topics, they are always very similar. So how you install electron microscope A is very similar to how you install electron microscope B, but it's the little differences that are really important if you are doing that installation procedure. If you forget one of those steps, of course, you will fail or you could even do some harm to the system. So it's really important that you not only have similar content or similarity in, let's say, the retrieval of the content, but you can actually know, "This is content for product A and this is content for product B."

Max:
So all of the work that went into structuring the content, adding metadata to each of the topics and connecting the metadata based on what entities are linkable, the RAG system that we implemented then, it could actually filter out all of the content that was not relevant to the specific question or use case. So the answers were quite good from the beginning.

Larry:
Yeah. I want to elaborate a bit on the evolution of your RAG architecture, and for folks who don't...

Quentin Reul

The complementary nature of knowledge graphs and LLMs has become clear, and long-time knowledge engineering professionals like Quentin Reul now routinely combine them in hybrid neuro-symbolic AI systems.

While it's tempting to get caught up in the details of rapidly advancing AI technology, Quentin emphasizes the importance of always staying focused on the business problems your systems are solving.

We talked about:

his extensive background in semantic technologies, dating back to the early 2000s
his contribution to the SKOS standard
an overview of the strengths and weaknesses of LLMs
the importance of entity resolution, especially when working with the general information that LLMs are trained on
how LLMs accelerate knowledge graph creation and population
his take on the scope of symbolic AI, in which he includes expert systems and rule-based systems
his approach to architecting neuro-symbolic systems, which always starts with, and stays focused on, the business problem he's trying to solve
his advice to avoid the temptation to start projects with technology, and instead always focus on the problems you're solving
the importance of staying abreast of technology developments so that you're always able to craft the most efficient solutions

Quentin's bio
Dr. Quentin Reul is an AI Strategy & Innovation Executive who bridges the gap between high-level business goals and deep technical implementation. As a Director of AI Strategy & Solutions at expert.ai, he specializes in the convergence of Generative AI, Knowledge Graphs, and Agentic Workflows. His focus is moving companies beyond "PoC Purgatory" into production-grade systems that deliver measurable ROI.

Unlike traditional strategists, he remains deeply hands-on, continuously prototyping with emerging AI research to stress-test its real-world impact. He doesn't just advocate for AI; he builds the technical roadmaps that translate the latest lab breakthroughs into safe, scalable, and high-value enterprise solutions.
Connect with Quentin online

LinkedIn
BlueSky
YouTube
Medium

Video
Here’s the video version of our conversation:

https://youtu.be/J8fgIezoNxE
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 44. We're far enough along now in the development of both generative AI learning models and symbolic AI technology like knowledge graphs to see the strengths and weaknesses of each. Quentin Reul has worked with both technologies, and the technologies that preceded them, for many years. He now builds systems that combine the best of both types of AI to deliver solutions that make it easier for people to discover and explore the knowledge and information that they need.
Interview transcript
Larry:
Hi, everyone. Welcome to episode number 44 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Quentin Reul. Quentin is the director of AI Strategy and solutions at expert.ai in the US in Chicago. So welcome, Quentin. Tell the folks a little bit more about what you're up to these days.

Quentin:
Hi, thank you, Larry, for accepting me and getting me on your podcast. So my name is Quentin Reul. I actually have been around the RDF and the knowledge graph since before it was cool in the early 2000. And today, what I'm helping people in news, media, and entertainment is to see how they can leverage all of the unstructured data that they have and make it in a way that can be structured and they can make their content more findable and discoverable as part of what they are offering to their customers.

Larry:
Nice. And I love that you've been doing this forever. And one of the things we talked about before we went on the air was your early involvement in the SKOS standard. Can you talk a little bit about your little contribution to that project?

Quentin:
Yeah. So for this, we do know what SKOS stands for Simple Knowledge Organization System. It's a standard that has been created by the W3C standard around 2005. And being at the University of Aberdeen in Scotland, we had a lot of involvement with the W3C voicing the web ontology language and SKOS.

Quentin:
For SKOS, I was actually working on my PhD, and the idea of my PhD was to look at two ontologies and trying to map entities from one ontology to the entities in the other one. And a lot of the approach that were taken at the time were either leveraging philosophical kind of representation. And there was not really a lot of things that were looking at linguistics. So the approach that we were taking was looking at WordNet and using the structure of WordNet and maps that to the linguistic information, so the labels that were associated with nodes in the taxonomy.

Quentin:
But to do that, we needed to have a structure that was transitive. And at the time, SKOS only had broader and narrower, and they didn't have the transitive property. So my contribution was to push for the W3C standard and SKOS to include the SKOS broaderTransitive and SKOS narrowerTransitive, so that I could now have that if A broader B and B broader C, that A broader C was also correct, and having that description logic structure that would enable that.

Larry:
Well, that's so cool. I love that you have your ideas are ensconced in this 20-year-old standard now. But hey, what I wanted to talk about today and really focus on, I know I was excited to get you on the show because you're doing a lot of work in the area of neuro-symbolic AI, the idea of integrating LLMs and other machine learning technologies with knowledge graphs and other symbolic AI stuff.

Larry:
It's one of those things that everybody's talking about, but I haven't had the chance to talk on the podcast with many people who are actually doing it. So I'm hoping that you can help the listeners take the leap from this conceptual understanding of the natural complimentary nature of them to actually putting them together in an enterprise architecture. I guess maybe start with the strengths and weaknesses of each of the kinds of AI that we're talking about here.

Quentin:
Yeah. So if we look at the history of AI, symbolic AI was a thing that came up in the '70s and led to the first AI winter and the second AI winter for that matter. But where they were very good was in the structure and the explainability. So if you aren't very well set set of rules or predictive kind of aspect, it would do it consistently, repeatably, and all of that type of things.

Quentin:
Now, when you were trying to adopt a rule-based system for new data, it would die off because you had never seen that or a new set of rules or a new set of business requirements, it would just not handle that. And that's where machine learning really helped in making that transition to where we are today.

Quentin:
And the LLM, contributing further to that, in as much as the machine learning was pretty good at dealing with new patterns, as long as it was similar to the data that you were training with. I think one thing that the LLMs have really shine is in the way that it's able to surface things that you were not predicting from the data.

Quentin:
One thing that I think that we could have predicted or seen from the data if we had LLMs back in 2020 is we could probably have seen the topic of COVID emerging a bit earlier than what it did. And the reason is, it's because it's very good at surfacing things that it's never seen before. It's able at interpreting the language and analyzing the language in its structure. And by the sentence structure, understanding that things are very similar, and you may use different words for them, but now you're able to interpret them.

Quentin:
So if we think about information retrieval in the '90s, 2000s, and even in the 2010s, the way that we were doing a lot of these things was using control vocabulary, CISORI, or other dictionaries, and they were used to do query expansion. So you add a keyword, you were looking in the dictionaries, the dictionary were doing an expansion, and then you add something else.

Quentin:
Well, now with the LLM, that kind of expansion is intuitive to the actual LLM because you had seen so many different aspect and so many occurrence of text that it can actually predict and see what these different terms are associated with a holistic concept.

Quentin:
Now, that's a good thing. On the bad thing, the LLMs don't have ... Well, they have a cutoff point or knowledge cutoff point, which means that when they are trained, they are trained of information that is in the past. So they're not always that great at predicting, especially current event or information about things that are happening today, they're not very good at that.

Quentin:
I think if I look at the data, generally between the release of a new model and the nature of the data or the cutoff point, it's about six months to a year. This is like going a bit slower now or shorter in terms, but you have to remember that the time that it takes to train these models, we're speaking about days, weeks, and sometime months as opposed to hours with machine learning models. So they're expensive as well from that perspective.

Quentin:
Another aspect that they don't have, it's a knowledge base to just take a higher level from a knowledge graph, like the knowledge base. So it's not able to disambiguate information in a large corpus. It's very good to do entity linking within the context of one document.

Quentin:
So if you pass it one document, let's say a financial document, and it refers to Acme as an enterprise, if Acme is mentioned several times during the document, it will infer that there is only one entity and that entity is Acme.

Quentin:
But now, imagine that you have a group of financial reports, and these financial reports refer to Acme, a bakery in Illinois, and Acme, a construction company in Maryland.

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,

Brad Bolliger

Brad Bolliger entered the knowledge graph space via enterprise software system design and data analytics. That background informs their pragmatic and strategic approach to the use of semantic technology in systems that facilitate information exchange across government agencies.

We talked about:

their work at EY (Ernst & Young) on data and analytics strategy assessments and enterprise software design and as a co-chair of the NIEMOpen Technical Architecture Committee
how their work on EY's Unified Justice Platform introduced them to the knowledge graph world
a quick overview of entity resolution
the NIEM standard, its origin in the wake of 9/11, its scope, how it's built and managed, and how governments use it
their pragmatic approach to ontology and vocabulary management
the benefits of the extensibility of the RDF format and knowledge graph technology
how entity-centric data modeling accelerates and facilitates systems evolution
their take on "analytics enablement engineering"
their approach to crafting AI-ready data and building AI-aware enterprise solutions
some of the neuro-symbolic AI architecture's they have seen and implemented
their call for more systems thinking and systems analysis to create more effective services that work together in a more ethical and effective way

Brad's bio
Bradley Bolliger (they/them) works in the AI & Data practice of Ernst & Young and serves as co-chair of the NIEMOpen Technical Architecture Committee, an OASIS open standards project for data interoperability.

Brad assists clients across various industries with optimizing data platform ecosystems, enhancing customer relationships, and leveraging advanced analytics tools and techniques in their digital transformation efforts. In addition to designing data platforms and AI/NLP systems, Brad has served in lead analyst roles for public sector information system modernization efforts, including major contact center data ecosystems and integrated criminal justice system environments, the latter of which would lead to the development of the UnifiedJusticePlatform.
Connect with Brad online

LinkedIn
Unified Justice Platform

Video
Here’s the video version of our conversation:

https://youtu.be/8XCmF3qXv1E
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 42. When you have to account for the people and other entities involved in high-stakes situations, you need a system that delivers accurate, unambiguous information. Brad Bolliger does this in their work on EY's Unified Justice Platform. Brad is relatively new to the graph world and has adopted a pragmatic approach to semantic modeling and knowledge graphs, focusing on applying lessons learned in their extensive experience in enterprise systems design and data analytics.
Interview transcript
Larry:
Hi, everyone. Welcome to episode number 42 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Brad Bolliger. Brad works in the AI and data practice at EY, the big consultancy in Chicago, and also helps co-chair the NIEM Information Exchange, the Info Exchange Network and standard. Welcome, Brad. Tell the folks a little bit more about what you're up to these days.

Brad:
Thanks for having me, Larry. I'm thrilled to be talking to you today. Yeah, I'm non-binary. I use they/them pronouns, and I work in the AI and data practice at Ernst & Young, as you said, where I do data and analytics strategy assessments and enterprise software design, things like that. I'm also co-chair of the NIEMOpen Technical Architecture Committee, which is an Oasis Open standard for sharing data in public services primarily, but for specification for developing information exchanges. And I'm working on semantics and software design more generally.

Larry:
Yeah. And you kind of not stumbled, but you had semantics thrust upon you in this new role, I understand, 'cause one of the projects you work on, I don't know if you're still working on it, was the Unified Justice Platform at EY. Can you talk a little bit about that and how it brought you into the semantics world?

Brad:
Yeah, that's right. It spun out of an assessment from a county government wanting to overhaul their integrated justice system, which was the collection of actors who collaborate or have this adversarial relationship to administer the process of justice in their jurisdiction. And because very often they're their own elected officials with their own budgets, they have their own software to fulfill their own functions. And that means that they are kind of inherently operating a distributed system, sending messages back and forth to say, "Hey, we booked this person into the jail. Hey, we've got this court date coming up. Hey, we're filing these charges." And they need to orchestrate complex operational processes across multiple software systems and multiple groups of people, again, kind of across jurisdictions or enclaves. And that was, of course, a really interesting systems analysis process that led to the development of a solution to this problem we were trying to assess, which we later called the Unified Justice Platform and is an event-driven architecture for building an entity-resolved knowledge graph as an operational data store programmatically as messages are exchanged between the stakeholders in the Enclave.

Larry:
Yeah. And you used a couple of words in there. I want to clarify for folks who might be new to them. The notion of entity resolution, the entity-resolved knowledge graph, I'll just point out that we met through our mutual friend, Paco Nathan, who works for Senzing, a company that just does entity resolution. And can you talk a little bit about entity resolution, how that fits into the needs of this distributed system and how you implement it in the platform?

Brad:
Yeah. Actually, I'll plug almost two years ago, we did a webinar with someone from Senzing and talked about the fundamental utility of entity resolution and relevance, I suppose, as a problem more generally. Entity resolution is essentially about creating, for me, is essentially about creating a high quality master index of whatever kind of data that it is that you're looking at. So in this case, we were talking about a master person index so that you have a more reliable picture of the same natural person, no matter which software system is representing the data that describes the person subject to judicial proceedings in particular. But thinking about entity-centric data modeling more generally, you got a different type of entity, you still need to disambiguate which location you're talking about, which person you're talking about, which entity that really is. And if there are different representations, different records that relate to the same underlying entity, that process of entity resolution therefore has this really broad systemic benefit to data management and data engineering in particular, because ultimately it's about the master index at the end of the day.

Larry:
Yeah. And as you talked about that, you mentioned that it's like this a canonical record of entities. And how does NIEM fit into that? Because that's a vocabulary as I understand it.

Brad:
That's right.

Larry:
Yeah. Can you talk a little bit about NIEM and how that works with entity resolution?

Brad:
Yeah, very briefly on NIEM, NIEM spun out of the post September 11th realization that public services needed to share data to collaborate more effectively to actually solve emergencies, but just problems in general. And what they realized was that they need to have a common language to collaborate more effectively. Again, because systems, machines, software systems, have this really concrete definition of we use these particular terms and they mean something in our enclave, but you could have a person's full name and a person's first name and a person's last name in two different records, but actually they're the same real person. So NIEM came out of an attempt to at least address some of that disambiguity. And what is most interesting to me about NIEM, honestly, is that it is a collaboratively defined list of vocabulary. So we actually get domain participants involved and they decide we use these terms and they mean these things.

Brad:
And so it's an attempt to reduce the amount of complexity that you could use to describe a different person, but communicate the same meaning without losing the information that's entailed in some data record. But I'm digressing a little bit probably. What NIEM is a framework for building message specifications, APIs, if you like, or other types of structures, data structures in general that is a community agreed-upon set of terms that have some kind of core relevance, person, entity, organization, or have some domain specific function, like, subject or something in human services and so on.

Larry:
Interesting. Yeah. And as you talk about that, that attempt to align people on vocabulary is such a notoriously difficult problem. And I don't know how many jurisdictions we're talking about here, but every little town in America has a police department and other social services that they do. What is the scope or the scale of that? And is it facilitated in any way by existing standards or vocabularies?

Brad:
Oh, very much so. In fact, the problem is even worse than you've described it very charitably, I think. Just in the United States alone, I'm told that there are over 18,000 law enforcement agencies, just law enforcement agencies. Nevermind how ... Anyway, so NIEM is a voluntary open standard. So it is something that is available, but is usually not mandated. There are some places where it is mandated for specific types of services. So the scale of the problem that we're talking about really depends on who's included in the conversation.