Knowledge Graph Insights

Casey Hart Ontology engineering has its roots in the idea of ontology as defined by classical philosophers. Casey Hart sees many other connections between professional ontology practice and the academic discipline of philosophy and shows how concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology in general. We talked about: his work as a lead ontologist at Ford and as an ontology consultant his academic background in philosophy the variety of pathways into ontology practice the philosophical principles like metaphysics, epistemology, and logic that inform the practice of ontology his history with the the Cyc project and employment at Cycorp how he re-uses classes like "category" and similar concepts from upper ontologies like gist his definition of "AI" - including his assertion that we should use term to talk about a practice, not a particular technology his reminder that ontologies are models and like all models can oversimplify reality Casey's bio Casey Hart is the lead ontologist for Ford, runs an ontology consultancy, and pilots a growing YouTube channel. He is enthusiastic about philosophy and ontology evangelism. After earning his PhD in philosophy from the University of Wisconsin-Madison (specializing in epistemology and the philosophy of science), he found himself in the private sector at Cycorp. Along his professional career, he has worked in several domains: healthcare, oil & gas, automotive, climate science, agriculture, and retail, among others. Casey believes strongly that ontology should be fun, accessible, resemble what is being modelled, and just as complex as it needs to be. He lives in the Pacific Northwest with his wife and three daughters and a few farm animals. Connect with Casey online LinkedIn ontologyexplained at gmail dot com Ontology Explained YouTube channel Video Here’s the video version of our conversation: https://youtu.be/siqwNncPPBw Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 38. When the subject of philosophy comes up in relation to ontology practice, it's typically cited as the origin of the term, and then the subject is dropped. Casey Hart sees many other connections between ontology practice and it its philosophical roots. In addition to logic as the foundation of OWL, he shows how philosophy concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology in general. Interview transcript Larry: Hi, everyone. Welcome to episode number 38 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Casey Hart. Casey has a really cool YouTube channel on the philosophy behind ontology engineering and ontology practice. Casey is currently an ontologist at Ford, the motor car company. So welcome Casey, tell the folks a little bit more about what you're up to these days. Casey: Hi. Thanks, Larry. I'm super excited to be here. I've listened to the podcast, and man, your intro sounds so smooth. I was like, "I wonder how many edits that takes." No, you just fire them off, that's beautiful. Casey: Yeah, so like you said, these days I'm the ontologist at Ford, so building out data models for sensor data and vehicle information, all those sorts of fun things. I am also working as a consultant. I've got a couple of different startup healthcare companies and some cybersecurity stuff, little things around the edge. I love evangelizing ontology, talking about it and thinking about it. And as you mentioned for the YouTube channel, that's been my creative outlet. My background is in philosophy and I was interested in, I got my PhD in philosophy, I was going to teach it. You write lots of papers, those sorts of things, and I miss that to some extent getting out into industry, and that's been my way back in to, all right, come up with an idea,

Chris Mungall Capturing knowledge in the life sciences is a huge undertaking. The scope of the field extends from the atomic level up to planetary-scale ecosystems, and a wide variety of disciplines collaborate on the research. Chris Mungall and his colleagues at the Berkeley Lab tackle this knowledge-management challenge with well-honed collaborative methods and AI-augmented computational tooling that streamlines the organization of these precious scientific discoveries. We talked about: his biosciences and genetics work at the Berkeley Lab how the complexity and the volume of biological data he works with led to his use of knowledge graphs his early background in AI his contributions to the gene ontology the unique role of bio-curators, non-semantic-tech biologists, in the biological ontology community the diverse range of collaborators involved in building knowledge graphs in the life sciences the variety of collaborative working styles that groups of bio-creators and ontologists have created some key lessons learned in his long history of working on large-scale, collaborative ontologies, key among them, meeting people where they are some of the facilitation methods used in his work, tools like GitHub, for example his group's decision early on to commit to version tracking, making change-tracking an entity in their technical infrastructure how he surfaces and manages the tacit assumptions that diverse collaborators bring to ontology projects how he's using AI and agentic technology in his ontology practice how their decision to adopt versioning early on has enabled them to more easily develop benchmarks and evaluations some of the successes he's had using AI in his knowledge graph work, for example, code refactoring, provenance tracking, and repairing broken links Chris's bio Chris Mungall is Department Head of Biosystems Data Science at Lawrence Berkeley National Laboratory. His research interests center around the capture, computational integration, and dissemination of biological research data, and the development of methods for using this data to elucidate biological mechanisms underpinning the health of humans and of the planet. He is particularly interested in developing and applying knowledge-based AI methods, particularly Knowledge Graphs (KGs) as an approach for integrating and reasoning over multiple types of data. Dr. Mungall and his team have led the creation of key biological ontologies for the integration of resources covering gene function, anatomy, phenotypes and the environment. He is a principal investigator on major projects such as the Gene Ontology (GO) Consortium, the Monarch Initiative, the NCATS Biomedical Data Translator, and the National Microbiome Data Collaborative project. Connect with Chris online LinkedIn Berkeley Lab Video Here’s the video version of our conversation: https://youtu.be/HMXKFQgjo5E Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 37. The span of the life sciences extends from the atomic level up to planetary ecosystems. Combine this scale and complexity with the variety of collaborators who manage information about the field, and you end up with a huge knowledge-management challenge. Chris Mungall and his colleagues have developed collaborative methods and computational tooling that enable the construction of ontologies and knowledge graphs that capture this crucial scientific knowledge. Interview transcript Larry: Hi everyone. Welcome to episode number 37 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Chris Mungall. Chris is a computational scientist working in the biosciences at the Lawrence Berkeley National Laboratory. Many people just call it the Berkeley Lab. He's the principal investigator in a group there, has his own lab working on a bunch of interesting stuff, which we're going to talk about today.

Emeka Okoye Semantic technologies permit powerful connections across a variety of linked data resources across the web. Until recently, developers had to learn the RDF language to discover and use these resources. Leveraging the new Model Context Protocol (MCP) and LLM-powered natural-language interfaces, Emeka Okoye has created the RDF Explorer, an MCP service that lets any developer surf the semantic web without having to learn its specialized language. We talked about: his long history in knowledge engineering and AI agents his deep involvement in the business and technology communities in Nigeria, including founding the country's first internet startup how he was building knowledge graphs before Google coined the term an overview of MCP, the Model Context Protocol, and its benefits the RDF Explorer MCP server he has developed how the MCP protocol and helps ease some of the challenges that semantic web developers have traditionally faced the capabilities of his RDF Explorer: facilitating communication between AI applications, language models, and RDF data enabling graph exploration and graph data analysis via SPARQL queries browsing, accessing, and evaluating linked-open-data RDF resources the origins of RDF Explorer in his attempt to improve ontology engineering tooling his objections to "vibe ontology" creation the ability of RDF Explorer to let non-RDF developers users access knowledge graph data how accessing knowledge graph data addresses the problem of the static nature of the data in language models the natural connections he sees between neural network AI and symbolic AI like knowledge graphs, and the tech tribalism he sees in the broader AI world that prevents others from seeing them how the ability of LLMs to predict likely language isn't true intelligence or actual knowledge some of the lessons he learned by building the RDF Explorer, e.g., how the MCP protocol removes a lot of the complexity in building hybrid AI solutions how MCP helps him validate the ontologies he creates Emeka's bio Emeka is a Knowledge Engineer, Semantic Architect, and Generative AI Engineer who leverages his over two decades of expertise in ontology and knowledge engineering and software development to architect, develop, and deploy innovative, data-centric AI products and intelligent cognitive systems to enable organizations in their Digital Transformation journey to enhance their data infrastructure, harness their data assets for high-level cognitive tasks and decision-making processes, and drive innovation and efficiency enroute to achieving their organizational goals. Emeka’s experience has embraced a breadth of technologies his primary focus being solution design, engineering and product development while working with a cross section of professionals across various cultures in Africa and Europe in solving problems at a complex level. Emeka can understand and explain technologies from deep diving under the hood to the value proposition level. Connect with Emeka online LinkedIn Making Knowledge Graphs Accessible: My Journey with MCP and RDF Explorer RDF Explorer (GitHub) Video Here’s the video version of our conversation: https://youtu.be/GK4cqtgYRfA Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 36. The widespread adoption of semantic technologies has created a variety of linked data resources on the web. Until recently, you had to learn semantic tools to access that data. The arrival of LLMs, with their conversational interfaces and ability to translate natural language into knowledge graph queries, combined with the new Model Context Protocol, has empowered semantic web experts like Emeka Okoye to build tools that let any developer surf the semantic web. Interview transcript Larry: Hi, everyone. Welcome to episode number 36 of the Knowledge Graph Insights podcast.

Tom Plasterer Shortly after the semantic web was introduced, the demand for discoverable and shareable data arose in both research and industry. Tom Plasterer was instrumental in the early conception and creation of the FAIR data principle, the idea that data should be findable, accessible, interoperable, and reusable. From its origins in the semantic web community, scientific research, and the pharmaceutical industry, the FAIR data idea has spread across academia, research, industry, and enterprises of all kinds. We talked about: his recent move from a big pharma company to Exponential Data where he leads the knowledge graph and FAIR data practices the direct line from the original semantic web concept to FAIR data principles the scope of the FAIR acronym, not just four concepts, but actually 15 how the accessibility requirement in FAIR distinguishes the standard from the open data the role of knowledge graphs in the implementation of a FAIR data program the intentional omission of prescribed implementations in the development of FAIR and the ensuing variety of implementation patterns how the desire for consensus in the biology community smoothed the development of the FAIR standard the role of knowledge graphs in providing a structure for sharing terminology and other information in a scientific community how his interest in omics led him to computer science and then to the people skills crucial to knowledge graph work the origins of the impetus for FAIR in European scientific research and the pharmaceutical industry the growing adoption of FAIR as enterprises mature their web thinking and vendors offer products to help with implementations the roles of both open science and the accessibility needs in industry contributed to the development of FAIR the interesting new space at the intersection of generative AI and FAIR and knowledge graph the crucial foundational role of FAIR in AI systems Tom's bio Dr. Tom Plasterer is a leading expert in data strategy and bioinformatics, specializing in the application of knowledge graphs and FAIR data principles within life sciences and healthcare. With over two decades of experience in both industry and academia, he has significantly contributed to bioinformatics, systems biology, biomarker discovery, and data stewardship. His entrepreneurial ventures include co-founding PanGenX, a Personalized Medicine/Pharmacogenetics Knowledge Base start-up, and directing Project Planning and Data Interpretation at BG Medicine. During his extensive tenure at AstraZeneca, he was instrumental in championing Data Centricity, FAIR Data, and Knowledge Graph initiatives across various IT and scientific business units. Currently, Dr. Plasterer serves as the Managing Director of Knowledge Graph and FAIR Data Capability at XponentL Data, where he defines strategy and implements advanced applications of FAIR data, knowledge graphs, and generative AI for the life science and healthcare industries. He is also a prominent figure in the community, having co-founded the Pistoia Alliance FAIR Data Implementation group and serving on its FAIR data advisory board. Additionally, he co-organizes the Health Care and Life Sciences symposium at the Knowledge Graph Conference and is a member of Elsevier’s Corporate Advisory Board. Connect with Tom online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/Lt9Dc0Jvr4c Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 35. With the introduction of semantic web technologies in the early 2000s, the World Wide Web began to look something like a giant database. And with great data, comes great responsibility. In response to the needs of data stewards and consumers across science, industry, and technology, the FAIR data principle - F A I R - was introduced. Tom Plasterer was instrumental in the early efforts to make web data findable,

Mara Inglezakis Owens Mara Inglezakis Owens brings a human-centered focus to her work as an enterprise architect at a major US airline. Drawing on her background in the humanities and her pragmatic approach to business, she has developed a practice that embodies both "digital anthropology" and product thinking. The result is a knowledge architecture that works for its users and consistently demonstrates its value to key stakeholders. We talked about: her role as an enterprise architect at a major US airline how her background as a humanities scholar, and especially as a rhetoric teacher, prepared her for her current work as a trusted business advisor some important mentoring she received early in her career how "digital anthropology" and product thinking fit into her enterprise architecture practice how she demonstrates the financial value of her work to executives and other stakeholders her thoughtful approach to the digitalization process and systems design the importance of documentation in knowledge engineering work how to sort out and document stakeholders' self-reports versus their actual behavior the scope of her knowledge modeling work, not just physical objects in the world, but also processes and procedures two important lessons she's learned over her career: don't be afraid to justify financial investment in your work, and "don't be so attached to an ideal outcome that you miss the best possible" Mara's bio Mara Inglezakis Owens is an enterprise architect who specializes in digitalization and knowledge management. She has deep experience in end-to-end supply chain as well as in planning, product, and program management. Mara’s background is in epistemology (history and philosophy of science, information science, and literature), which gives a unique, humanistic flavor to her practice. When she is not working, Mara enjoys aviation, creative writing, gardening, and raising her children. She lives in Minneapolis. Connect with Mara online LinkedIn email: mara dot inglezakis dot owens at gmail dot com Video Here’s the video version of our conversation: https://youtu.be/d8JUkq8bMIc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 34. When think about architecting knowledge systems for a giant business like a global airline, you might picture huge databases and complex spaghetti diagrams of enterprise architectures. These do in fact exist, but the thing that actually makes these systems work is an understanding of the needs of the people who use, manage, and finance them. That's the important, human-focused work that Mara Inglezakis Owens does as an enterprise architect at a major US airline. Interview transcript Larry: Hi, everyone. Welcome to episode 34 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, Mara, I'm going to get this right, Inglezakis Owens. She's an enterprise architect at a major US airline. So, welcome, Mara. Tell the folks a little bit more about what you're up to these days. Mara: Hi, everybody. My name's Mara. And these days I am achieving my childhood dream of working in aviation, not as a pilot, but that'll happen, but as an enterprise architect. I've been doing EA, also data and information architecture, across the whole scope of supply chain for about 10 years, everything from commodity sourcing to SaaS, software as a service, to now logistics. And a lot of my days, I spend interviewing subject matter experts, convincing business leaders they should do stuff, and on my best days, I get to crawl around on my hands and knees in an airplane hangar. Larry: Oh, fun. That is ... Yeah. I didn't know ... I knew that there's that great picture of you sitting in the jet engine, but I didn't realize this was the fulfillment of a childhood dream. That's awesome. But everything you've just said ties in so well to the tagline on your LinkedIn pro...

Frank van Harmelen Much of the conversation around AI architectures lately is about neuro-symbolic systems that combine neural-network learning tech like LLMs and symbolic AI like knowledge graphs. Frank van Harmelen's research has followed this path, but he puts all of his AI research in the larger context of how these technical systems can best support people. While some in the AI world seek to replace humans with machines, Frank focuses on AI systems that collaborate effectively with people. We talked about: his role as a professor of AI at the Vrije Universiteit in Amsterdam how rapid change in the AI world has affected the 10-year, €20-million Hybrid Intelligence Centre research he oversees the focus of his research on the hybrid combination of human and machine intelligence how the introduction of conversational interfaces has advance AI-human collaboration a few of the benefits of hybrid human-AI collaboration the importance of a shared worldview in any collaborative effort the role of the psychological concept of "theory of mind" in hybrid human-AI systems the emergence of neuro-symbolic solutions how he helps his students see the differences between systems 1 and 2 thinking and its relevance in AI systems his role in establishing the foundations of the semantic web the challenges of running a program that spans seven universities and employs dozens of faculty and PhD students some examples of use cases for hybrid AI-human systems his take on agentic AI, and the importance of humans in agent systems some classic research on multi-agent computer systems the four research challenges - collaboration, adaptation, responsibility, and explainability - they are tackling in their hybrid intelligence research his take on the different approaches to AI in Europe, the US, and China the matrix structure he uses to allocate people and resources to three key research areas: problems, solutions, and evaluation his belief that "AI is there to collaborate with people and not to replace us" Frank's bio Since 2000 Frank van Harmelen has played a leading role in the development of the Semantic Web. He is a co-designer of the Web Ontology Language OWL, which has become a worldwide standard. He co-authored the first academic textbook of the field, and was one of the architects of Sesame, an RDF storage and retrieval engine, which is in wide academic and industrial use. This work received the 10-year impact award at the International Semantic Web Conference. Linked Open Data and Knowledge Graphs are important spin-offs from this work. Since 2020, Frank is is scientific director of the Hybrid Intelligence Centre, where 50 PhD students and as many faculty members from 7 Dutch universities investigate AI systems that collaborate with people instead of replacing them. The large scale of modern knowledge graphs that contain hundreds of millions of entities and relationships (made possible partly by the work of Van Harmelen and his team) opened the door to combine these symbolic knowledge representations with machine learning. Since 2018, Frank has pivoted his research group from purely symbolic Knowledge Representation to Neuro-Symbolic forms of AI. Connect with Frank online Hybrid Intelligence Centre Video Here’s the video version of our conversation: https://youtu.be/ox20_l67R7I Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 33. As the AI landscape has evolved over the past few years, hybrid architectures that combine LLMs, knowledge graphs, and other AI technology have become the norm. Frank van Harmelen argues that the ultimate hybrid system must also include humans. He's running a 10-year, €20 million research program in the Netherlands to explore exactly this. His Hybrid Intelligence Centre investigates AI systems that collaborate with people instead of replacing them. Interview transcript Larry: Hi,

Denny Vrandečić As the founder of Wikidata, Denny Vrandečić has thought a lot about how to better connect the world's knowledge. His current project is Abstract Wikipedia, an initiative that aims to let anyone anywhere on the planet contribute to, and benefit from, the world's collective knowledge, in their native language. It's an ambitious goal, but - inspired by the success of other contributor-driven Wikimedia Foundation projects - Denny is confident that community can make it happen We talked about: his work as Head of Special Projects at the Wikimedia Foundation and his current projects: Wikifunctions and Abstract Wikipedia the origin story of his first project at Wikimedia - Wikidata a precursor project that informed Wikidata - Semantic MediaWiki the resounding success of the Wikidata project, the most edited wiki in the world, with half a million contributors how the need for more expressivity than Wikidata offers led to the idea for Abstract Wikipedia an overview of the Abstract Wikipedia project the abstract language-independent notation that underlies Abstract Wikipedia how Abstract Wikipedia will permit almost instant updating of Wikipedia pages with the facts it provides the capability of Abstract Wikipedia to permit both editing and use of knowledge in an author's native language their exploration of using LLMs to use natural language to create structured representations of knowledge how the design of Abstract Wikipedia encourages and facilitates contributions to the project the Wikifunctions project, a necessary precondition to Abstract Wikipedia the role of Wikidata as the Rosetta Stone of the web some background on the Wikifunctions project the community outreach work that Wikimedia Foundation does and the role of the community in the development of Abstract Wikipedia and Wikifunctions the technical foundations for his how to contribute to Wikimedia Foundation projects his goal to remove language barriers to allow all people to work together in a shared knowledge space a reminder that Tim Berners-Lee's original web browser included an editing function Denny's bio Denny Vrandečić is Head of Special Projects at the Wikimedia Foundation, leading the development of Wikifunctions and Abstract Wikipedia. He is the founder of Wikidata, co-creator of Semantic MediaWiki, and former elected member of the Wikimedia Foundation Board of Trustees. He worked for Google on the Google Knowledge Graph. He has a PhD in Semantic Web and Knowledge Representation from the Karlsruhe Institute of Technology. Connect with Denny online user Denny at Wikimedia Wikidata profile Mastodon LinkedIn email: denny at wikimedia dot org Resources mentioned in this interview Wikimedia Foundation Wikidata Semantic MediaWiki Wikidata: The Making Of Wikifunctions Abstract Wikipedia Meta-Wiki Video Here’s the video version of our conversation: https://youtu.be/iB6luu0w_Jk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 32. The original plan for the World Wide Web was that it would be a two-way street, with opportunities to both discover and share knowledge. That promise was lost early on - and then restored a few years later when Wikipedia added an "edit" button to the internet. Denny Vrandečić is working to make that edit function even more powerful with Abstract Wikipedia, an innovative platform that lets web citizens both create and consume the world's knowledge, in their own language. Interview transcript Larry: Hi, everyone. Welcome to episode number 32 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Denny Vrandecic. Denny is best known as the founder of Wikidata, which we'll talk about more in just a minute. He's currently the Head of Special Projects at the Wikimedia Foundation. He's also a visiting professor at King's College Lo...

Charles Ivie Since the semantic web was introduced almost 25 years ago, many have dismissed it as a failure. Charles Ivie shows that the RDF standard and the knowledge-representation technology built on it have actually been quite successful. More than half of the world's web pages now share semantic annotations and the widespread adoption of knowledge graphs in enterprises and media companies is only growing as enterprise AI architectures mature. We talked about: his long work history in the knowledge graph world his observation that the semantic web is "the most catastrophically successful thing which people have called a failure" some of the measures of the success of the semantic web: ubiquitous RDF annotations in web pages, numerous knowledge graph deployments in big enterprises and media companies, etc. the long history of knowledge representation the role of RDF as a Rosetta Stone between human knowledge and computing capabilities how the abstraction that RDF permits helps connect different views of knowledge within a domain the need to scope any ontology in a specific domain the role of upper ontologies his transition from computer science and software engineering to semantic web technologies the fundamental role of knowledge representation tech - to help humans communicate information, to innovate, and to solve problems how semantic modeling's focus on humans working things out leads to better solutions than tech-driven approaches his desire to start a conversation around the fundamental upper principles of ontology design and semantic modeling, and his hypothesis that it might look something like a network of taxonomies Charles' bio Charles Ivie is a Senior Graph Architect with the Amazon Neptune team at Amazon Web Services (AWS). With over 15 years of experience in the knowledge graph community, he has been instrumental in designing, leading, and implementing graph solutions across various industries. Connect with Charles online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/1ANaFs-4hE4 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 31. Since the concept of the semantic web was introduced almost 25 years ago, many have dismissed it as a failure. Charles Ivie points out that it's actually been a rousing success. From the ubiquitous presence of RDF annotations in web pages to the mass adoption of knowledge graphs in enterprises and media companies, the semantic web has been here all along and only continues to grow as more companies discover the benefits of knowledge-representation technology. Interview transcript Larry: Hi everyone. Welcome to episode number 31 of the Knowledge Graph Insights Podcast. I am really happy today to welcome to the show Charles Ivie. Charles is currently a senior graph architect at Amazon's Neptune department. He's been in the graph community for years, worked at the BBC, ran his own consultancies, worked at places like The Telegraph and The Financial Times and places you've heard of. So welcome Charles. Tell the folks a little bit more about what you're up to these days. Charles: Sure. Thanks. Thanks, Larry. Very grateful to be invited on, so thank you for that. And what have I been up to? Yeah, I've been about in the graph industry for about 14 years or something like that now. And these days I am working with the Amazon Neptune team doing everything I can to help people become more successful with their graph implementations and with their projects. And I like to talk at conferences and join things like this and write as much as I can. And occasionally they let me loose on some code too. So that's kind of what I'm up to these days. Larry: Nice. Because you have a background as a software engineer and we will talk more about that later because I think that's really relevant to a lot of what we'll talk about.

Andrea Gioia In recent years, data products have emerged as a solution to the enterprise problem of siloed data and knowledge. Andrea Gioia helps his clients build composable, reusable data products so they can capitalize on the value in their data assets. Built around collaboratively developed ontologies, these data products evolve into something that might also be called a knowledge product. We talked about: his work as CTO at Quantyca, a data and metadata management consultancy his description of data products and their lifecycle how the lack of reusability in most data products inspired his current approach to modular, composable data products - and brought him into the world of ontology how focusing on specific data assets facilitates the creation of reusable data products his take on the role of data as a valuable enterprise asset how he accounts for technical metadata and conceptual metadata in his modeling work his preference for a federated model in the development of enterprise ontologies the evolution of his data architecture thinking from a central-governance model to a federated model the importance of including the right variety business stakeholders in the design of the ontology for a knowledge product his observation that semantic model is mostly about people, and working with them to come to agreements about how they each see their domain Andrea's bio Andrea Gioia is a Partner and CTO at Quantyca, a consulting company specializing in data management. He is also a co-founder of blindata.io, a SaaS platform focused on data governance and compliance. With over two decades of experience in the field, Andrea has led cross-functional teams in the successful execution of complex data projects across diverse market sectors, ranging from banking and utilities to retail and industry. In his current role as CTO at Quantyca, Andrea primarily focuses on advisory, helping clients define and execute their data strategy with a strong emphasis on organizational and change management issues. Actively involved in the data community, Andrea is a regular speaker, writer, and author of 'Managing Data as a Product'. Currently, he is the main organizer of the Data Engineering Italian Meetup and leads the Open Data Mesh Initiative. Within this initiative, Andrea has published the data product descriptor open specification and is guiding the development of the open-source ODM Platform to support the automation of the data product lifecycle. Andrea is an active member of DAMA and, since 2023, has been part of the scientific committee of the DAMA Italian Chapter. Connect with Andrea online LinkedIn (#TheDataJoy) Github Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=g34K_kJGZMc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 30. In the world of enterprise architectures, data products are emerging as a solution to the problem of siloed data and knowledge. As a data and metadata management consultant, Andrea Gioia helps his clients realize the value in their data assets by assembling them into composable, reusable data products. Built around collaboratively developed ontologies, these data products evolve into something that might also be called a knowledge product. Interview transcript Larry: Hi, everyone. Welcome to episode number 30 of the Knowledge Graph Insights podcast. I'm really happy today to welcome to the show Andrea Gioia. Andrea's, he does a lot of stuff. He's a busy guy. He's a partner and the chief technical officer at Quantyca, a consulting firm that works on data and metadata management. He's the founder of Blindata, a SaaS product that goes with his consultancy. I let him talk a little bit more about that. He's the author of the book Managing Data as a Product, and he's also, he comes out of the data heritage but he's now one of these knowledge people like us.

Dave McComb During the course of his 25-year consulting career, Dave McComb has discovered both a foundational problem in enterprise architectures and the solution to it. The problem lies in application-focused software engineering that results in an inefficient explosion of redundant solutions that draw on overlapping data sources. The solution that Dave has introduced is a data-centric architecture approach that treats data like the precious business asset that it is. We talked about: his work as the CEO of Semantic Arts, a prominent semantic technology and knowledge graph consultancy based in the US the application-centric quagmire that most modern enterprises find themselves trapped in data centricity, the antidote to application centricity his early work in semantic modeling how the discovery of the "core model" in an enterprise facilitates modeling and building data-centric enterprise systems the importance of "baby step" approaches and working with actual customer data in enterprise data projects how building to "enduring business themes" rather than to the needs of individual applications creates a more solid foundation for enterprise architectures his current interest in developing a semantic model for the accounting field, drawing on his history in the field and on Semantic Arts' gist upper ontology the importance of the concept of a "commitment" in an accounting model how his approach to financial modeling permits near-real-time reporting his Data-Centric Architecture Forum, a practitioner-focused event held each June in Ft. Collins, Colorado Dave's bio Dave McComb is the CEO of Semantic Arts. In 2000 he co-founded Semantic Arts with the aim of bringing semantic technology to Enterprises. From 2000- 2010 Semantic Arts focused on ways to improve enterprise architecture through ontology modeling and design. Around 2010 Semantic Arts began helping clients more directly with implementation, which led to the use of Knowledge Graphs in Enterprises. Semantic Arts has conducted over 100 successful projects with a number of well know firms including Morgan Stanley, Electronic Arts, Amgen, Standard & Poors, Schneider-Electric, MD Anderson, the International Monetary Fund, Procter & Gamble, Goldman Sachs as well as a number of government agencies. Dave is the author of Semantics in Business Systems (2003), which made the case for using Semantics to improve the design of information systems, Software Wasteland (2018) which points out how application-centric thinking has led to the deplorable state of enterprise systems and The Data-Centric Revolution (2019) which outlines a alternative to the application-centric quagmire. Prior to founding Semantic Arts he was VP of Engineering for Velocity Healthcare, a dot com startup that pioneered the model driven approach to software development. He was granted three patents on the architecture developed at Velocity. Prior to that he was with a small consulting firm: First Principles Consulting. Prior to that he was part of the problem. Connect with Dave online LinkedIn email: mccomb at semanticarts dot com Semantic Arts Resources mentioned in this interview Dave's books: The Data-Centric Revolution: Restoring Sanity to Enterprise Information Systems Software Wasteland: How the Application-Centric Quagmire is Hobbling Our Enterprises Semantics in Business Systems: The Savvy Manager's Guide gist ontology Data-Centric Architecture Forum Video Here’s the video version of our conversation: https://youtu.be/X_hZG7cFOCE Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 29. Every modern enterprise wrestles with its data, trying to get the most out of it. The smartest businesses have figured out that it isn't just "the new oil" - data is the very bedrock of their enterprise architecture. For the past 25 years, Dave McComb has helped companies understand the...

Ole Olesen-Bagneux In every enterprise, says Ole Olesen-Bagneux, the information you need to understand your organization's metadata is already there. It just needs to be discovered and documented. Ole's Meta Grid can be as simple as a shared, curated collection of documents, diagrams, and data but might also be expressed as a knowledge graph. Ole appreciates "North Star" architectures like microservices and the Data Mesh but presents the Meta Grid as a simpler way to manage enterprise metadata. We talked about: his work as Chief Evangelist at Actian his forthcoming book, "Fundamentals of Metadata Management" how he defines his Meta Grid: an integration architecture that connects metadata across metadata repositories his definition of metadata and its key characteristic, that it's always in two places at once how the Meta Grid compares with microservices architectures and organizing concepts like Data Mesh the nature of the Meta Grid as a small, simple, and slow architecture which is not technically difficult to achieve his assertion that you can't build a Meta Grid because it already exists in every organization the elements of the Meta Grid: documents, diagrams or pictures, and examples of data how knowledge graphs fit into the Meta Grid his appreciation for "North Star" architectures like Data Mesh but also how he sees the Meta Grid as a more pragmatic approach to enterprise metadata management the evolution of his new book from a knowledge graph book to his elaboration on the "slow" nature of the Meta Grid, in particular how its metadata focus contrasts with faster real-time systems like ERPs the shape of the team topology that makes Meta Grid work Ole's bio Ole Olesen-Bagneux is a globally recognized thought leader in metadata management and enterprise data architecture. As VP, Chief Evangelist at Actian, he drives industry awareness and adoption of modern approaches to data intelligence, drawing on his extensive expertise in data management, metadata, data catalogs, and decentralized architectures. An accomplished author, Ole has written The Enterprise Data Catalog (O’Reilly, 2023). He is currently working on Fundamentals of Metadata Management (O’Reilly, 2025), introducing a novel metadata architecture known as the Meta Grid. With a PhD in Library and Information Science from the University of Copenhagen, his unique perspective bridges traditional information science with modern data management. Before joining Actian, Ole served as Chief Evangelist at Zeenea, where he played a key role in shaping and communicating the company’s technology vision. His industry experience includes leadership roles in enterprise architecture and data strategy at major pharmaceutical companies like Novo Nordisk.Ole is passionate about scalable metadata architectures, knowledge graphs, and enabling organizations to make data truly discoverable and usable. Connect with Ole online LinkedIn Substack Medium Resources mentioned in this interview Fundamentals of Metadata Management, Ole's forthcoming book Data Management at Scale by Piethein Strengholt Fundamentals of Data Engineering by Joe Reis and Matt Housley Meta Grid as a Team Topology, Substack article Stewart Brand's Pace Layers Video Here’s the video version of our conversation: https://youtu.be/t01IZoegKRI Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 28. Every modern enterprise wrestles with the scale, the complexity, and the urgency of understanding their data and metadata. So, by necessity, comprehensive architectural approaches like microservices and the data mesh are complex, big, and fast. Ole Olesen-Bagneux proposes a simple, small, and slow way for enterprises to cultivate a shared understanding of their enterprise knowledge, a decentralized approach to metadata strategy that he calls the Meta Grid. Interview transcript Larry: Hi,

Andrea Volpini Your organization's brand is what people say about you after you've left the room. It's the memories you create that determine how people think about you later. Andrea Volpini says that the same dynamic applies in marketing to AI systems. Modern brand managers, he argues, need to understand how both human and machine memory work and then use that knowledge to create digital memories that align with how AI systems understand the world. We talked about: his work as CEO at WordLift, a company that builds knowledge graphs to help companies automate SEO and other marketing activities a recent experiment he did during a talk at an AI conference that illustrates the ability of applications like Grok and ChatGPT to build and share information in real time the role of memory in marketing to current AI architectures his discovery of how the agentic approach he was taking to automating marketing tasks was actually creating valuable context for AI systems the mechanisms of memory in AI systems and an analogy to human short- and long-term memory the similarities he sees in how the human neocortex forms memories and how the knowledge about memory is represented in AI systems his practice of representing entities as both triples and vectors in his knowledge graph how he leverages his understanding of the differences in AI models in his work the different types of memory frameworks to account for in both the consumption and creation of AI systems: semantic, episodic, and procedural his new way of thinking about marketing: as a memory-creation process the shift in focus that he thinks marketers need to make, "creating good memories for AI in order to protect their brand values" Andrea's bio Andrea Volpini is the CEO of WordLift and co-founder of Insideout10. With 25 years of experience in semantic web technologies, SEO, and artificial intelligence, he specializes in marketing strategies. He is a regular speaker at international conferences, including SXSW, TNW Conference, BrightonSEO, The Knowledge Graph Conference, G50, Connected Data and AI Festival. Andrea has contributed to industry publications, including the Web Almanac by HTTP Archive. In 2013, he co-founded RedLink GmbH, a commercial spin-off focused on semantic content enrichment, natural language processing, and information extraction. Connect with Andrea online LinkedIn X Bluesky WordLift Video Here’s the video version of our conversation: https://youtu.be/do-Y7w47CZc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 27. Some experts describe the marketing concept of branding as, What people say about you after you’ve left the room. It's the memories they form of your company that define your brand. Andrea Volpini sees this same dynamic unfolding as companies turn their attention to AI. To build a memorable brand online, modern marketers need to understand how both human and machine memory work and then focus on creating memories that align with how AI systems understand the world. Interview transcript Larry: Hi, everyone. Welcome to episode number 27 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Andrea Volpini. Andrea is the CEO and the founder at WordLift, a company based in Rome. Tell the folks a little bit more about WordLift and what you're up to these days, Andrea. Andrea: Yep. So we build knowledge graphs and to help brands automate their SEO and marketing efforts using large language model and AI in general. Larry: Nice. Yeah, and you're pretty good at this. You've been doing this a while and you had a recent success story, I think that shows, that really highlights some of your current interests in your current work. Tell me about your talk in Milan and the little demonstration you did with that. Andrea: Yeah, yeah, so it was last week at AI Festival,

Jacobus Geluk The arrival of AI agents creates urgency around the need to guide and govern them. Drawing on his 15-year history in building reliable AI solutions for banks and other enterprises, Jacobus Geluk sees a standards-based data-product marketplace as the key to creating the thriving data economy that will enable AI agents to succeed at scale. Jacobus launched the effort to create the DPROD data-product description specification, creating the supply side of the data market. He's now forming a working group to document the demand side, a "use-case tree" specification to articulate the business needs that data products address. We talked about: his work as CEO at Agnos.ai, an enterprise knowledge graph and AI consultancy the working group he founded in 2023 which resulted in the DPROD specification to describe data products an overview of the data-product marketplace and the data economy the need to account for the demand side of the data marketplace the intent of his current work on to address the disconnect between tech activities and business use cases how the capabilities of LLMs and knowledge graphs complement each other the origins of his "use-case tree" model in a huge banking enterprise knowledge graph he built ten years ago how use case trees improve LLM-driven multi-agent architectures some examples of the persona-driven, tech-agnostic solutions in agent architectures that use-case trees support the importance of constraining LLM action with a control layer that governs agent activities, accounting for security, data sourcing, and issues like data lineage and provenance the new Use Case Tree Work Group he is forming the paradox in the semantic technology industry now of a lack of standards in a field with its roots in W3C standards Jacobus' bio Jacobus Geluk is a Dutch Semantic Technology Architect and CEO of agnos.ai, a UK-based consulting firm with a global team of experts specializing in GraphAI — the combination of Enterprise Knowledge Graphs (EKG) with Generative AI (GenAI). Jacobus has over 20 years of experience in data management and semantic technologies, previously serving as a Senior Data Architect at Bloomberg and Fellow Architect at BNY Mellon, where he led the first large-scale production EKG in the financial industry. As a founding member and current co-chair of the Enterprise Knowledge Graph Forum (EKGF), Jacobus initiated the Data Product Workgroup, which developed the Data Product Ontology (DPROD) — a proposed OMG standard for consistent data product management across platforms. Jacobus can claim to have coined the term "Enterprise Knowledge Graph (EKG)" more than 10 years ago, and his work has been instrumental in advancing semantic technologies in financial services and other information-intensive industries. Connect with Jacobus online LinkedIn Agnos.ai Resources mentioned in this podcast DPROD specification Enterprise Knowledge Graph Forum Object Management Group Use Case Tree Method for Business Capabilities DCAT Data Catalog Vocabulary Video Here’s the video version of our conversation: https://youtu.be/J0JXkvizxGo Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 26. In an AI landscape that will soon include huge groups of independent software agents acting on behalf of humans, we'll need solid mechanisms to guide the actions of those agents. Jacobus Geluk looks at this situation from the perspective of the data economy, specifically the data-products marketplace. He helped develop the DPROD specification that describes data products and is now focused on developing use-case trees that describe the business needs that they address. Interview transcript Larry: Okay. Hi everyone. Welcome to episode number 26 of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show, Jacobus Geluk. Sorry, I try to speak Dutch, do my best.

Rebecca Schneider Skills that Rebecca Schneider learned in library science school - taxonomy, ontology, and semantic modeling - have only become more valuable with the arrival of AI technologies like LLMs and the growing interest in knowledge graphs. Two things have stayed constant across her library and enterprise content strategy work: organizational rigor and the need to always focus on people and their needs. We talked about: her work as Co-Founder and Executive Director at AvenueCX, an enterprise content strategy consultancy her background as a "recovering librarian" and her focus on taxonomies, metadata, and structured content the importance of structured content in LLMs and other AI applications how she balances the capabilities of AI architectures and the needs of the humans that contribute to them the need to disambiguate the terms that describe the span of the semantic spectrum the crucial role of organization in her work and how you don't to have formally studied library science to do it the role of a service mentality in knowledge graph work how she measures the efficiency and other benefits of well-organized information how domain modeling and content modeling work together in her work her tech-agnostic approach to consulting the role of metadata strategy into her work how new AI tools permit easier content tagging and better governance the importance of "knowing your collection," not becoming a true subject matter expert but at least getting familiar with the content you are working with the need to clean up your content and data to build successful AI applications Rebecca's bio Rebecca is co-founder of AvenueCX, an enterprise content strategy consultancy. Her areas of expertise include content strategy, taxonomy development, and structured content. She has guided content strategy in a variety of industries: automotive, semiconductors, telecommunications, retail, and financial services. Connect with Rebecca online LinkedIn email: rschneider at avenuecx dot com Video Here’s the video version of our conversation: https://youtu.be/ex8Z7aXmR0o Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 25. If you've ever visited the reference desk at your local library, you've seen the service mentality that librarians bring to their work. Rebecca Schneider brings that same sensibility to her content and knowledge graph consulting. Like all digital practitioners, her projects now include a lot more AI, but her work remains grounded in the fundamentals she learned studying library science: organizational rigor and a focus on people and their needs. Interview transcript Larry: Hi, everyone. Welcome to episode number 25 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Rebecca Schneider. Rebecca is the co-founder and the executive director at AvenueCX, a consultancy in the Boston area. Welcome, Rebecca. Tell the folks a little bit more about what you're up to these days. Rebecca: Hi, Larry. Thanks for having me on your show. Hello, everyone. My name is Rebecca Schneider. I am a recovering librarian. I was a trained librarian, worked in a library with actual books, but for most of my career, I have been focusing on enterprise content strategy. Furthermore, I typically focus on taxonomies, metadata, structured content, and all of that wonderful world that we live in. Larry: Yeah, and we both come out of that content background and have sort of converged on the knowledge graph background together kind of over the same time period. And it's really interesting, like those skills that you mentioned, the library science skills of taxonomy, metadata, structured, and then the application of that in structured content in the content world, how, as you've got in more and more into knowledge graph stuff, how has that background, I guess...

Ashleigh Faith With her 15-year history in the knowledge graph industry and her popular YouTube channel, Ashleigh Faith has informed and inspired a generation of graph practitioners and enthusiasts. She's an expert on semantic modeling, knowledge graph construction, and AI architectures and talks about those concepts in ways that resonate both with her colleagues and with newcomers to the field. We talked about: her popular IsA DataThing YouTube channel the crucial role of accurately modeling actual facts in semantic practice and AI architectures her appreciation of the role of knowledge graphs in aligning people in large organizations around concepts and the various words that describe them the importance of staying focused on the business case for knowledge graph work, which has become both more important with the arrival of LLMs and generative AI the emergence of more intuitive "talk to your graph" interfaces some of her checklist items for onboarding aspiring knowledge graph engineers how to decide whether to use a property graph or a knowledge graph, or both her hope that more RDF graph vendors will offer a free tier so that people can more easily experiment with them approaches to AI architecture orchestration the enduring importance of understanding how information retrieval works Ashleigh's bio Ashleigh Faith has her PhD in Advanced Semantics and over 15 years of experience working on graph solutions across the STEM, government, and finance industries. Outside of her day-job, she is the Founder and host of the IsA DataThing YouTube channel and podcast where she tries to demystify the graph space. Connect with Ashleigh online LinkedIn IsA DataThing YouTube channel Video Here’s the video version of our conversation: https://youtu.be/eMqLydDu6oY Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 24. One way to understand the entity resolution capabilities of knowledge graphs is to picture on old-fashioned telephone operator moving plugs around a switchboard to make the right connections. Early in her career, that's one way that Ashleigh Faith saw the power of knowledge graphs. She has since developed sophisticated approaches to knowledge graph construction, semantic modeling, and AI architectures and shares her deeply informed insights on her popular YouTube channel. Interview transcript Larry: Hi, everyone. Welcome to episode number 24 of the Knowledge Graph Insights Podcast. I am super extra delighted today to welcome to the show Ashleigh Faith. Ashleigh is the host of the awesome YouTube channel IsA DataThing, which has thousands of subscribers, thousands of monthly views. I think it's many people's entry point into the knowledge graph world. Welcome, Ashleigh. Great to have you here. Tell the folks a little bit more about what you're up to these days. Ashleigh: Thanks, Larry. I've known you for quite some time. I'm really excited to be here today. What about me? I do a lot of semantic and AI stuff for my day job. But yeah, I think my main passion is also helping others get involved, understand some of the concepts a little bit better for the semantic space and now the neuro-symbolic AI. That's AI and knowledge graphs coming together. That is quite a hot topic right now, so lots and lots of untapped potential in what we can talk about. I do most of that on my channel. Larry: Yeah. I will refer people to your channel because we've got only a half-hour today. It's ridiculous. Ashleigh: Yeah. Larry: We just talked for an hour before we went on the air. It's ridiculous. What I'd really like to focus on today is the first stage in any of this, the first step in any of these knowledge graph implementations or any of this stuff is modeling. I think about it from a designerly perspective. I do a lot of mental model discernment, user research kind of stuff, and then conceptual modeling to agree on things.

Panos Alexopoulos Any knowledge graph or other semantic artifact must be modeled before it's built. Panos Alexopoulos has been building semantic models since 2006. In 2020, O'Reilly published his book on the subject, "Semantic Modeling for Data." The book covers the craft of semantic data modeling, the pitfalls practitioners are likely to encounter, and the dilemmas they'll need to overcome. We talked about: his work as Head of Ontology at Textkernel and his 18-year history working with symbolic AI and semantic modeling his definition and description of the practice of semantic modeling and its three main characteristics: accuracy, explicitness, and agreement the variety of artifacts that can result from semantic modeling: database schemas, taxonomies, hierarchies, glossaries, thesauri, ontologies, etc. the difference between identifying entities with human understandable descriptions in symbolic AI and numerical encodings in sub-symbolic AI the role of semantic modeling in RAG and other hybrid AI architectures a brief overview of data modeling as a practice how LLMs fit into semantic modeling: as sources of information to populate a knowledge graph, as coding assistants, and in entity and relation extraction other techniques besides NLP and LLMs that he uses in his modeling practice: syntactic patterns, heuristics, regular expressions, etc. the role of semantic modeling and symbolic AI in emerging hybrid AI architectures the importance of defining the notion of "autonomy" as AI agents emerge Panos' bio Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as a principal educator at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world. Connect with Panos online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/ENothdlfYGA Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 23. In order to build a knowledge graph or any other semantic artifact, you first need to model the concepts you're working with, and that model needs to be accurate, to explicitly represent all of the ideas you're working with, and to capture human agreements about them. Panos Alexopoulos literally wrote the book on semantic modeling for data, covering both the principles of modeling as well as the pragmatic concerns of real-world modelers. Interview transcript Larry: Hi everyone. Welcome to episode number 23 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Panos Alexopoulos. Panos is the head of ontology at Textkernel, a company in Amsterdam that works on knowledge graphs for the HR and recruitment world. Welcome, Panos. Tell the folks a little bit more about what you're doing these days. Panos: Hi Larry. Thank you very much for inviting me to your podcast. I'm really happy to be here. Yeah, so as you said, I'm head of ontology at Textkernel. Actually, I've been working in the field of data semantics, knowledge graph ontologies for almost now 18 years, even before the era of machine learning,

Mike Pool Mike Pool sees irony in the fact that semantic-technology practitioners struggle to use the word "semantics" in ways that meaningfully advance conversations about their knowlege-representation work. In a recent LinkedIn post, Mike even proposed a moratorium on the use of the word. We talked about: his multi-decade career in knowledge representation and ontology practice his opinion that we might benefit from a moratorium on the term "semantics" the challenges in pinning down the exact scope of semantic technology how semantic tech permits reusability and enables scalability the balance in semantic practice between 1) ascribing meaning in tech architectures independent of its use in applications and 2) considering end-use cases the importance of staying domain-focused as you do semantic work how to stay pragmatic in your choice of semantic methods how reification of objects is not inherently semantic but does create a framework for discovering meaning how to understand and capture subtle differences in meaning of seemingly clear terms like "merger" or "customer" how LLMs can facilitate capturing meaning Mike's bio Michael Pool works in the Office of the CTO at Bloomberg, where he is working on a tool to create and deploy ontologies across the firm. Previously, he was a principal ontologist on the Amazon Product Knowledge team, and has also worked to deploy semantic technologies/approaches and enterprise knowledge graphs at a number of big banks in New York City. Michael also spent a couple of years on the famous Cyc project and has evaluated knowledge representation technologies for DARPA. He has also worked on tooling to integrate probabilistic and semantic models and oversaw development of an ontology to support a consumer-facing semantic search engine. He lives in New York City and loves to run around in circles in Central Park. Connect with Mike online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/JlJjBWGwSDg Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 22. The word "semantics" is often used imprecisely by semantic-technology practitioners. It can describe a wide array of knowledge-representation practices, from simple glossaries and taxonomies to full-blown enterprise ontologies, any of which may be summarized in a conversation as "semantics." Mike Pool thinks that this dynamic - using a word that lacks precise meaning while assuming that it communicates a lot - may justify a moratorium on the use of the term. Interview transcript Larry: Hi everyone, welcome to episode number 22 of the Knowledge Graph Insights podcast. I'm really happy today to welcome to the show Mike Pool. Mike is a longtime ontologist, a couple of decades plus. He recently took a position at Bloomberg. But he made this really provocative post on LinkedIn lately that I want to flesh out today, and we'll talk more about that throughout the rest of the show. Welcome, Mike, tell the folks a little bit more about what you're up to these days. Mike: Hey, thank you, Larry. Yeah. As you noted, I've just taken a position with Bloomberg and for these many years that you alluded to, I've been very heavily focused on building, doing knowledge representation in general. In the last let's say decade or so I've been particularly focused on using ontologies and knowledge graphs in large banks, or large organizations at least, to help organize disparate data, to make it more accessible, breakdown data silos, et cetera. It's particularly relevant in the finance industry where things can be sliced and diced in so many different ways. I find there's a really important use case in the financial space but in large organizations in general, in my opinion, for using ontology. So that's a lot of what I've been thinking about, to make that more accessible to the organization and to help them build these ontologies and utilize th...

Margaret Warren As a 10-year-old photographer, Margaret Warren would jot down on the back of each printed photo metadata about who took the picture, who was in it, and where it was taken. Her interest in image metadata continued into her adult life, culminating the creation of ImageSnippets, a service that lets anyone add linked open data descriptions to their images. We talked about: her work to make images more discoverable with metadata connected via a knowledge graph how her early childhood history as a metadata strategist, her background in computing technology, and her personal interest in art and a photography shows up in her product, ImageSnippets her takes on the basics of metadata strategy and practice the many types of metadata: descriptive, administrative, technical, etc. the role of metadata in the new AI world some of the good and bad reasons that social media platforms might remove metadata from images privacy implications of metadata in social media the linked data principles that she applies in ImageSnippets and how they're managed in the product's workflow her wish that CMSs and social media platforms would not strip the metadata from images as they ingest them the lightweight image ontology that underlies her ImageSnippets product her prediction that the importance of metadata that supports provenance, demonstrates originality, and sets context will continue to grow in the future Margaret's bio Margaret Warren is a technologist, researcher and artist/content creator. She is the founder and CEO of Metadata Authoring Systems whose mission is to make the most obscure images on the web findable, and easily accessible by describing and preserving them in the most precise ways possible. To assist with this mission, she is the creator of a system called, ImageSnippets which can be used by anyone to build linked data descriptions of images into graphs. She is also a research associate with the Florida Institute of Human and Machine Cognition, one of the primary organizers of a group called The Dataworthy Collective and is a member of the IPTC (International Press and Telecommunications Council) photo-metadata working group and the Research Data Alliance charter on Collections as Data. As a researcher, Margaret's primary focus is at the intersection of semantics, metadata, knowledge representation and information science particularly around visual content, search and findability. She is deeply interested in how people describe what they experience visually and how to capture and formalize this knowledge into machine readable structures. She creates tools and processes for humans but augmented by machine intelligence. Many of these tools are useful for unifying the many types of metadata and descriptions of images - including the very important context element - into ontology infused knowledge graphs. Her tools can be used for tasks as advanced as complex domain modeling but can also facilitate image content to be shared and published while staying linked to it's metadata across workflows. Learn more and connect with Margaret online LinkedIn Patreon Bluesky Substack ImageSnippets Metadata Authoring Systems personal and art site IPTC links IPTC Photo Metadata Software that supports IPTC Photo Metadata Get IPTC Photo Metadata Browser extensions for IPTC Photo Metadata Resource not mentioned in podcast (but very useful for examining structured metadata in web pages) OpenLink Structured Data Sniffer (OSDS) Video Here’s the video version of our conversation: https://youtu.be/pjoAAq5zuRk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 21. Nowadays, we are all immersed in a deluge of information and media, especially images. The real value of these images is captured in the metadata about them. Without information about the history of an image, its technical details,

Jans Aasman Hybrid AI architectures get more complex every day. For Jans Aasman, large language models and generative AI are just the newest additions to his toolkit. Jans has been building advanced hybrid AI systems for more than 15 years, using knowledge graphs, symbolic logic, and machine learning - and now LLMs and gen AI - to build advanced AI systems for Fortune 500 companies. We talked about: his knowledge graph and neuro-symbolic work as the CEO of Franz the crucial role of a visionary knowledge graph champion in KG adoption in enterprises the two types of KG champions he has encountered: the magic-seeking, forward-looking technologist and the more pragmatic IT leader trying to better organize their operation the AI architectural patterns and themes he has seen emerge over the past 25 years: logic, reasoning, event-based KGs, machine learning, and of course gen AI and LLMs how gen AI lets him do things he couldn't have imagined five years ago the enduring importance of enterprise taxonomies, especially in RAG architectures which business entities need to be understood to answer complex business questions his approach to neuro-symbolic AI, seeing it as a "fluid interplay between a knowledge graph, symbolic logic, machine learning, and generative AI" the power of "magic predicates" a common combination of AI technologies and human interactions that can improve medical diagnosis and care decisions his strong belief in keeping humans in the loop in AI systems his observation that technology and business leaders seeing the need for "a symbolic approach next to generative AI" his take on the development of reasoning capabilities of LLMs how the code-generation capabilities of LLMs are more beneficial to senior programmers and may even impede the work of less experiences coders Jans' bio Jans Aasman is a Ph.D. psychologist and expert in Cognitive Science - as well as CEO of Franz Inc., an early innovator in Artificial Intelligence and provider of Knowledge Graph Solutions based on AllegroGraph. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as government entities worldwide. Connect with Jans online LinkedIn email: ja at franz dot com Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=SZBZxC8S1Uk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 20. The mix of technologies in hybrid artificial intelligence systems just keeps getting more interesting. This might seem like a new phenomenon, but long before our LinkedIn feeds were clogged with posts about retrieval augmented generation and neuro-symbolic architectures, Jans Aasman was building AI systems that combined knowledge graphs, symbolic logic, and machine learning. Large language models and generative AI are just the newest technologies in his AI toolkit. Interview transcript Larry: Hi, everyone. Welcome to episode number 20 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Jans Aasmann. Jans is, he originally started out as a psychologist and he got into cognitive science. For the past 20 years, he's run a company called Franz, where he's the CEO doing neuro-symbolic AI, so welcome, Jans. Tell the folks a little bit more about what you're doing these days. Jans: We help companies build knowledge graphs, but with the special angle that we now offer neuro-symbolic AI so that we, in a very fluid way, mix traditional symbolic logic and the traditional machine learning with the new generative AI. We do this in every possible combination that you could think of. Larry: Who? Jans: These applications might be in healthcare or in call centers or in publishing. It's many, many, many different domains it supplies. Larry:

Juan Sequeda Knowledge graph technology has been around for decades. The benefits so far accruing to only a few big enterprises and tech companies. Juan Sequeda sees large language models as a critical enabler for the broader adoption of KGs. With their capacity to accelerate the acquisition and use of valuable business knowledge, LLMs offer a path to a better return on your enterprise's investment in semantics. We talked about: his work data.world as Principal scientist and the head of the AI lab at data.world the new discovery and knowledge-acquisition capabilities that LLMs give knowledge engineers a variety of business benefits that unfold from these new capabilities the payoff of investing in semantics and knowledge: "one plus one is greater than two" how semantic understanding and the move from a data-first world to a knowledge-first world helps businesses make better decisions and become more efficient the pendulum swings in the history of the development of AI and knowledge systems his research with Dean Allemang on how knowledge graphs can help LLMs improve the accuracy of answers of questions posed to enterprise relational databases the role of industry benchmarks in understanding the return on your invest in semantics the importance of treating semantics as a first-class citizen how business leaders can recognize and take advantage of the semantics and knowledge work that is already happening in their organizations Juan's bio Juan Sequeda is the Principal Scientist and Head of the AI Lab at data.world. He holds a PhD in Computer Science from The University of Texas at Austin. Juan’s research and industry work has been on the intersection of data and AI, with the goal to reliably create knowledge from inscrutable data, specifically designing and building Knowledge Graph for enterprise data and metadata management. Juan is the co-author of the book “Designing and Building Enterprise Knowledge Graph” and the co-host of Catalog and Cocktails, an honest, no-bs, non-salesy data podcast. Connect with Juan online LinkedIn Catalog & Cocktails podcast Video Here’s the video version of our conversation: https://youtu.be/xZq12K7GvB8 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 19. The AI pendulum has been swinging back and forth for many decades. Juan Sequeda argues that we're now at a point in the advancement of AI technology where businesses can fully reap its long-promised benefits. The key is a semantic understanding of your business, captured in a knowledge graph. Juan sees large language models as a critical enabler of this capability, in particular the ability of LLMs to accelerate the acquisition and use of valuable business knowledge. Interview transcript Larry: Hi, everyone. Welcome to episode number 19 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Juan Sequeda. Juan is the principal scientist and the head of the AI lab at data.world. He's also the co-host of the really good popular podcast, Catalog & Cocktails. So welcome, Juan. Tell the folks a little bit more about what you're up to these days. Juan: Hey, very great. Thank you so much for having me. Great to chat with you. So what am I up to now these days? Obviously, knowledge graphs is something that is my entire life of what I've been doing. This was before it was called knowledge graphs. I would say that the last year, year-and-a-half, almost two years now, I would say, is been understanding the relationship between knowledge graphs and LLMs. If people have been following our work, what we've been doing a lot has been on understanding how to use knowledge graphs to increase the accuracy for your chat with your data system, so be able to do question answering over your structured SQL databases and how knowledge graphs increase the accuracy of that. So we can chat about that. Juan: