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Larry Swanson

Knowledge Graph Insights

A Technology , Business and Management podcast

#173 in Technology · 🇸🇬
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About the show

Interviews with experts on semantic technology, ontology design and engineering, linked data, and the semantic web.

Newest episodes

David Newbury: Pointers, Proxies, and Modeling Complexity – Episode 59

31 min

David Newbury Modeling data and knowledge always involves trade-offs. At the Getty Trust, David Newbury manages what he calls a “complexity budget,” focusing on the concepts his users are most concerned with. For example, they carefully model time for art historians while downplaying geospatial data. Getty shares the resulting structured knowledge in two ways: as simple reference documents and as a rich knowledge graph that helps researchers answer questions that no one has asked before. We talked about: his role as Senior Director of Public Technologies at the Getty Trust how Getty settled on semantic technology to manage and share knowledge about its extensive collections the differences between how an organization like Getty thinks about its collections and how its users access that information the unique benefits that an independent cultural heritage organization has over commercial entities when it comes to knowledge architectures his academic work on "pointers and proxies" — knowledge organization that helps users find things vs. metadata and other information that lets researchers digitally model items in their collection to conduct research the philosophy and practical considerations that underlie their modeling practices their allocation of a good portion of their "complexity budget" to modeling time, since that is such important information for art historians and other researchers the crucial role of linking in their metadata and modeling strategy the origin of the concept of provenance in the field of art history the two different ways they talk about provenance at Getty: their Provenance Index, a huge collection of art-transaction receipts, and their historical records of the people, places, and things collected their goal to serve as a source of ground truth about objects in their collections, providing the same information both in reference documents and in complex graphs that let people ask research questions that Getty staff may not ha...

François Scharffe: An Agentic Harness for Knowledge Engineers – Episode 58

36 min

François Scharffe As AI unleashes a host of new tools and agents to help build knowledge graphs, engineers like François Scharffe are tasked with harnessing these new capabilities and deploying them effectively. François brings decades of AI experience to the goal he set for himself earlier this year: to automate his own job. He's making good progress, but he's also finding that there's still plenty of need for him and other humans in the workflows he's designing. We talked about: the upcoming 2027 edition of the Knowledge Graph Conference his long history of working with various kinds of AI the distinctions between knowledge engineering and software engineering the rationales behind his choice of Pi as his coding harness an overview of his harness set-up and knowledge engineering workflow the benefits to knowledge graph building that AI brings the variety of tools and agents he has built for tasks like requirements specification, data sourcing, ontology design, term extraction and re-use, entity resolution, data mappings, ETL pipelines, etc. the evaluation processes he's using to assess the benefits of his new workflows the ongoing need for humans in the loop, particularly for tasks like ontology creation how he has adapted his work style for the AI era, in particular the number of tasks he now does in parallel with AI how he manages ongoing improvements to his system - tracking decision traces and implementing introspection steps as well as devising scenarios and using "critic" agents to improve processes the reason he downplays prompt writing and review in his operation in favor of system evaluations his scientific approach to system evaluation, aiming for reproducibility when moving from one model to another the focus on agent memory in the 2027 Knowledge Graph Conference program François' bio Francois Scharffe is a hands-on enterprise knowledge architect and semantic AI leader. He has built and stewarded production knowledge graphs across sev...

Jessica Talisman: The Ontology Pipeline Knowledge Engineering Framework – Episode 57

33 min

Jessica Talisman When the idea of the semantic spectrum was introduced 25 years ago, it was meant to be a conceptual tool to help programmers and architects understand the relative benefits of different levels of semantic effort. Now, a couple of decades later, Jessica Talisman has repurposed the model as a way to organize the actual process of developing enterprise knowledge infrastructure, which she shares in her new book, "Ontology Pipeline: A Framework for Knowledge Engineering." We talked about: her new book, "Ontology Pipeline: A Framework for Knowledge Engineering" the inspiration she took from Obrst's original semantic spectrum her language-first approach to ontology her pre-RDF semantic work with standards like Dublin Core the three "warrants" from library science that guide functional requirements specification the origins of her book in a 2018 Department of Justice project and its conceptual development in subsequent projects how the ontology pipeline emerged from her process of incrementally delivering artifacts in projects the importance of teasing out and addressing assumptions that can derail ontology projects the crucial need to meet an organization where its at in terms of its current semantic capabilities how her education-first approach helps get enterprise buy-in for ontology projects how using RDF in vocabulary and taxonomy work paves the way to broader ontology work the power of working with concept graphs, in particular its benefits in AI architectures the benefits of symbolic representations of concepts like attribution and provenance in AI systems the dual nature of her book as both an instruction manual and an ongoing reference resource Jessica's bio Jessica Talisman is a Semantic Engineer and Information Architect with more than 25 years of experience building knowledge infrastructure across enterprise architecture, e-commerce content systems, digital libraries, and knowledge management. She has led semantic architectur...

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All episodes

David Newbury: Pointers, Proxies, and Modeling Complexity – Episode 59

31 min

David Newbury Modeling data and knowledge always involves trade-offs. At the Getty Trust, David Newbury manages what he calls a “complexity budget,” focusing on the concepts his users are most concerned with. For example, they carefully model time for art historians while downplaying geospatial data. Getty shares the resulting structured knowledge in two ways: as simple reference documents and as a rich knowledge graph that helps researchers answer questions that no one has asked before. We talked about: his role as Senior Director of Public Technologies at the Getty Trust how Getty settled on semantic technology to manage and share knowledge about its extensive collections the differences between how an organization like Getty thinks about its collections and how its users access that information the unique benefits that an independent cultural heritage organization has over commercial entities when it comes to knowledge architectures his academic work on "pointers and proxies" — knowledge organization that helps users find things vs. metadata and other information that lets researchers digitally model items in their collection to conduct research the philosophy and practical considerations that underlie their modeling practices their allocation of a good portion of their "complexity budget" to modeling time, since that is such important information for art historians and other researchers the crucial role of linking in their metadata and modeling strategy the origin of the concept of provenance in the field of art history the two different ways they talk about provenance at Getty: their Provenance Index, a huge collection of art-transaction receipts, and their historical records of the people, places, and things collected their goal to serve as a source of ground truth about objects in their collections, providing the same information both in reference documents and in complex graphs that let people ask research questions that Getty staff may not ha...

François Scharffe: An Agentic Harness for Knowledge Engineers – Episode 58

36 min

François Scharffe As AI unleashes a host of new tools and agents to help build knowledge graphs, engineers like François Scharffe are tasked with harnessing these new capabilities and deploying them effectively. François brings decades of AI experience to the goal he set for himself earlier this year: to automate his own job. He's making good progress, but he's also finding that there's still plenty of need for him and other humans in the workflows he's designing. We talked about: the upcoming 2027 edition of the Knowledge Graph Conference his long history of working with various kinds of AI the distinctions between knowledge engineering and software engineering the rationales behind his choice of Pi as his coding harness an overview of his harness set-up and knowledge engineering workflow the benefits to knowledge graph building that AI brings the variety of tools and agents he has built for tasks like requirements specification, data sourcing, ontology design, term extraction and re-use, entity resolution, data mappings, ETL pipelines, etc. the evaluation processes he's using to assess the benefits of his new workflows the ongoing need for humans in the loop, particularly for tasks like ontology creation how he has adapted his work style for the AI era, in particular the number of tasks he now does in parallel with AI how he manages ongoing improvements to his system - tracking decision traces and implementing introspection steps as well as devising scenarios and using "critic" agents to improve processes the reason he downplays prompt writing and review in his operation in favor of system evaluations his scientific approach to system evaluation, aiming for reproducibility when moving from one model to another the focus on agent memory in the 2027 Knowledge Graph Conference program François' bio Francois Scharffe is a hands-on enterprise knowledge architect and semantic AI leader. He has built and stewarded production knowledge graphs across sev...

Jessica Talisman: The Ontology Pipeline Knowledge Engineering Framework – Episode 57

33 min

Jessica Talisman When the idea of the semantic spectrum was introduced 25 years ago, it was meant to be a conceptual tool to help programmers and architects understand the relative benefits of different levels of semantic effort. Now, a couple of decades later, Jessica Talisman has repurposed the model as a way to organize the actual process of developing enterprise knowledge infrastructure, which she shares in her new book, "Ontology Pipeline: A Framework for Knowledge Engineering." We talked about: her new book, "Ontology Pipeline: A Framework for Knowledge Engineering" the inspiration she took from Obrst's original semantic spectrum her language-first approach to ontology her pre-RDF semantic work with standards like Dublin Core the three "warrants" from library science that guide functional requirements specification the origins of her book in a 2018 Department of Justice project and its conceptual development in subsequent projects how the ontology pipeline emerged from her process of incrementally delivering artifacts in projects the importance of teasing out and addressing assumptions that can derail ontology projects the crucial need to meet an organization where its at in terms of its current semantic capabilities how her education-first approach helps get enterprise buy-in for ontology projects how using RDF in vocabulary and taxonomy work paves the way to broader ontology work the power of working with concept graphs, in particular its benefits in AI architectures the benefits of symbolic representations of concepts like attribution and provenance in AI systems the dual nature of her book as both an instruction manual and an ongoing reference resource Jessica's bio Jessica Talisman is a Semantic Engineer and Information Architect with more than 25 years of experience building knowledge infrastructure across enterprise architecture, e-commerce content systems, digital libraries, and knowledge management. She has led semantic architectur...

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

38 min

Eric Little Knowledge graphs are complicated. They might entail multiple ontologies, several data sources, and any number of generated graphs and other context-establishing elements. Eric Little came up with the idea of "super domains" to understand and manage these important but ephemeral pieces of an enterprise's semantic knowledge architecture. A super domain might establish context that holds for just a few seconds for an ATM transaction or for several years for a clinical trial. We talked about: his recent move from Accenture to Knowledge3 as Chief Data Officer his diverse background in the consulting and enterprise worlds super domains, the conceptual framework he has developed to deal with generated graphs, reification, and other knowledge graph elements how super domains capture context some examples of super domains how super domains can help you deal with temporary, ephemeral, and stochastic data how his philosophy background led to his early exploration of the concept of context graphs the crucial distinction that needs to be made between epistemology and ontology in semantic practice the role of phenomenology in his conception and practice of ontology his take on the current state of semantic technology practice how generative AI has brought the importance of metadata to the fore and highlights the need for deterministic capabilities in AI architectures the dearth of tooling in the semantic space and the need to deliver "semantics at the speed of AI" Eric's bio Eric Little, PhD is Chief Data Officer at Knowledge3. He previously was Industry Innovation Principal Director & Head of Semantic Strategy & AI for Global Assets at Accenture. He received a dual Ph.D. in Philosophy and Cognitive Science in 2002 from the University at Buffalo, State University of New York. His Post-Doctoral Fellowship at the University at Buffalo’s Department of Industrial Engineering (2002-2004) focused on developing ontologies for multisource information fusion a...

Dougal Watt: Human-Guided, LLM-Assisted Ontology Modeling – Episode 55

33 min

Dougal Watt Most conversations about taming generative AI focus on guardrails bolted on after the fact. Dougal Watt takes a different approach: generate the meaning first, as an ontology, and let everything else — agents, APIs, knowledge graphs — flow from that. His standing-room-only workshop on AI-augmented ontology creation at the Knowledge Graph Conference drew both business executives and semantic engineers, highlighting the broad interest in the need for accurate, trustworthy AI. We talked about: his work at the company he founded, Graph Research Labs and their meaning-first approach his very popular workshop on AI safety and ontologies and guardrails at the Knowledge Graph Conference the implications in AI architectures of the need to balance time, risk, and regulation the five main approaches that organizations are using to comply with regulations: guardrails multi-agent refinement governance frameworks sandboxing of agents ontology grounding how his Semantic Agent Harness delivers accuracy improvements better than even the best graph RAG systems how they build decision tracing into their framework the need to keep a human in the loop across the ontology-building process, as well as the crucial role of human judgement in automation workflows how LLMs can accelerate the task of integrating data in various organization silos how agentic modeling permits ongoing consistency checks on competency questions the need for more ontologists in AI work, but also tooling that he has created to facilitate ontology work how good ontology methods give organizations accuracy that is tailored to their unique knowledge Dougal's bio Dougal Watt is the CEO and Co-Founder of Graph Research Labs, inventor of multiple patents and patents pending covering ontology-driven declarative generation, governed AI agents, and automated enterprise stack creation. Previously IBM Chief Technologist and a global expert in information architecture. Open Group Distinguishe...

Yann Le Franc: From Semantic Silos to Shared Ontology Practice – Episode 54

35 min

Yann Le Franc The emerging field of neurosymbolic AI — combining LLMs and knowledge graphs in hybrid AI architectures — is new to a lot of people. For Yann Le Franc, it's the story of his career — from his academic days as a computational neuroscientist to his current work creating enterprise systems that ground LLMs in ontology-backed knowledge graphs. Over his extensive research and consulting career, Yann has honed his ontology design process, which he's now sharing in a platform that guides others in applying those time-tested ontology practices. We talked about: his work on the European Open Science Cloud, the LUMEN and GRAPHIA projects, and his KG and AI consultancy his academic evolution from cellular biology to neuroscience and then to computational neuroscience, neural network research, and neuroinformatics — and how that led to his discovery of the power of ontologies his current work to connect LLMs and ontologies a presentation on ontology-backed AI that he delivered at the Cultive Ta Data (Cultivate Your Data) conference earlier this year the refreshing emergence of the word ontology as a buzzword (as opposed to its "bad word" status only a couple of years ago) how he applies his research and innovation work to improve enterprise information systems his work to advance the practices of ontology design and knowledge graph building the inspiration he has taken from Robert Stevens to approach ontology development in a more aligned (non-siloed) way his ensuing adoption of the Linked Open Term methodology, an agile approach to ontology, and his broader work to reconnect "semantic silos" his journey into proper philosophically grounded ontology practice, and the notable absence of a canonical source of best practices the work at the LUMEN to develop a collaborative platform that guides practitioners to the tools available to each step in the ontology process how an enterprise ontology is the "skeleton of your whole information system, a repre...

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

40 min

Ben Rode As the Cyc project winds down, one of its main contributors, Benjamin Rode, is attempting to migrate as much as possible of its upper ontology to RDF. It's an ambitious project, much like Cyc itself. Cyc arose from the early AI research community, and Ben has a fascinating perspective on that milieu as well. We talked about: his work to port as much as possible of the Cyc upper ontology into RDF the surprisingly long history of neuro-symbolic AI some interesting details and anecdotes about the origins of the field of AI the deep intertwining of the histories of symbolic AI and computing itself Doug Lenat and the origins of the Cyc project and the Cycorp company Lenat's concept of "white space knowledge," the framing for unstructured natural language text how an attempt to port the Cyc ontology to RDF is "not as insane an exercise as it might sound at first" some parallels and distinction between the RDF and Cyc worlds the unique characteristics and capabilities of the CycL language, in particular its homoiconicity the central question for the current era: "can these two frameworks (symbolic AI and probabIlistic AI) work together in a synergistic way" Ben's bio Ben Rode came to formal domain modeling and ontology engineering by way of Douglas Hofstadter’s Gödel, Escher, Bach: The Eternal Golden Braid, which he read in high school. His interest has since developed into study of machine learning, causal inference and induction, temporal reasoning, ontology evolution, and neurosymbolics. He holds a graduate degree in philosophy with philosophy of mind and analytic philosophy as areas-of-focus; the subject of his dissertation was the use of formalized contexts in common sense reasoning. He joined the technical staff at Cycorp in 1997, where he's played an active role in developing the Cyc ontology for a number of contracts, including extensive work on database schema integration, ontology extension and mapping, inference development, and domain kn...

Lulit Tesfaye: Semantic Architectures for the AI Era – Episode 52

34 min

Lulit Tesfaye Generative AI has prompted a flurry of experimentation in enterprises, resulting in a parade of failed pilots, unproven PoCs, and unrealized return on investment. Lulit Tesfaye helps companies improve and optimize their AI capabilities by adding semantics to their enterprise architectures. This puts their precious knowledge assets in a context that machines can actually work with. A crucial part of that context is the interoperability that standards like RDF enable. We talked about: her work at Enterprise Knowledge as Partner & Vice President, Knowledge, Data, and AI Solutions the publication of Bridging Knowledge, Data, and AI: Harnessing the Semantic Layer Framework to Drive Intelligence, which she co-authored with colleagues at EK her professional path from software engineering and applications development to her current role how generative AI has increased data teams' workloads, leading to numerous pilots and PoCs, which has exposed the need in enterprises for semantic context her definition of a semantic layer "knowledge assets," a foundational concept that she uses to talk about data, content, information, expertise, and any other assets to which can append metadata how she and colleagues see the distinction between the two types of semantic layers: data and analytics layers and semantically modeled layers, and the benefits of the latter the framework she uses to convey the benefits of a semantic layer to executives the swing of the enterprise architecture pendulum back towards monoliths, as enterprise solutions providers like ServiceNow acquire semantic tool companies the importance of interoperability standards like RDF in semantic systems examples of use cases that are best suited to labeled property graphs and RDF knowledge graphs and how she sees the debate about which to use going away soon two common failure points in semantic layer projects: starting with tools, not competency questions insufficient skillsets and lack o...

Giancarlo Guizzardi: Ontology, Semantics, and Explainable AI – Episode 51

32 min

Giancarlo Guizzardi For nearly three decades, Giancarlo Guizzardi has researched and advanced the field of semantics and the practice of ontology and conceptual modeling. His work on the Unified Foundational Ontology (UFO), the OntoUML pattern language, and AI explainability are just a few of the accomplishments that make him an exemplar of the "full-stack ontologist." We talked about: his broad-ranging ontology and other responsibilities work at the University of Twente in the Netherlands the origins of the term ontology in computer science in 1967 George Mealy's assertion that "every data makes an ontological commitment" his take on the idea of capital O Ontology, both the conceptual tooling to build ontologies as digital artifacts and the design patterns that guide their creation how his insight that conceptual modeling is the foundation of any system led to his development of the Unified Foundational Ontology (UFO) his goal with UFO to give engineers tooling to reuse ontology patterns without having to expose them to the complexity of the underlying ontology itself the resulting OntoUML pattern language his belief that ontology engineering should separate conceptual modeling from design and implementation his take on the difference between verification and validation in ontology design how conceptual modeling and engineering implementation often end up in the hands of a "full-stack ontologist" how the ideas in his paper on "Explanation, Semantics, and Ontology" support explainable AI Giancarlo's bio Giancarlo Guizzardi is a Full Professor of Computer Science the University of Twente, The Netherlands, where he chairs the Semantics, Cybersecurity & Services (SCS) department. He is also a co-founder and co-director of the NeXAI Competence Cluster in the same university. He has been active for nearly three decades in the areas of Formal and Applied Ontology, Ontology Engineering, Conceptual Modeling, Enterprise Computing and Information Systems Engin...

Ora Lassila and Adrian Gschwend: RDF 1.2 Working Group Update – Episode 50

35 min

Ora Lassila and Adrian Gschwend Even as RDF has become ubiquitous in enterprises and across the web, its awkward handling of reification — the ability to refer to other statements in a graph — has limited its wider adoption. RDF 1.2 addresses this with the reifier: a new element that lets you attach provenance, confidence, and source directly to a relationship — including claims you're tracking but not asserting as true. We talked about: Ora and Adrian's extensive experience and backgrounds in the RDF community how the need to better handle reification led to the development of RDF 1.2 the W3C's use of the term "recommendation," which many/most people would think of as a "standard" the recent advancement of the RDF Concepts and Abstract Syntax and RDF Semantics specifications to "candidate recommendation" status the new RDF triple term - and how it permits references to RDF statements that have not been asserted some of the details that made reification difficult in prior versions of RDF: verbosity, inability to scale, etc. how the ability to reason on data distinguishes RDF graphs from labeled property graphs the high quality of the RDF 1.2 W3C working group and their confidence that their work has accounted for all of the important considerations that might arise the challenges of dealing with the needs for both backward and forward compatibility how committee specifications like RDF 1.2 compare with less collaborative vendor specifications how RDF saves BMW millions of lines of code when reasoning over car features how standards-setting has evolved over time, from codifying existing practices 30 years ago to more proactive approaches today Adrian's appreciation for the working group volunteer contributors and how they exemplify the values of open standards, open source, and open data Ora's observation about the truly open and transparent nature of the working group and the many benefits open standards, including the ability to avoid vendor lock-...

Daniel Davis: Grounding Generative AI with Context Graphs – Episode 49

38 min

Daniel Davis Long before Foundation Capital published their "trillion dollar opportunity" article about them, Daniel Davis had been building a platform for context graphs. Daniel's work in complex domains like aircraft safety and autonomous vehicles, as well as his study of quantum mechanics, gave him insights that led him to explore ways to ground probabilistic AI systems in the logic and knowledge they'd need to deliver trustworthy information. He settled on context graphs as the best way to accomplish this. Daniel was introduced to knowledge graphs by his co-founder Mark Adams, and he has immediately become an RDF evangelist, aiming to not only proselytize the tech but to also make Mark's cat Fred famous in the process. We talked about: his role as co-founder at TrustGraph his work to make his co-founder Mark Adam's cat Fred famous his diverse background in defense, autonomous vehicles, and cybersecurity how the complexity and vast scope of compliance requirements around autonomous vehicles led to his interest in context graphs how the arrival of ChatGPT and GPT-3, and his knowledge that probabilistic systems wouldn't be up to the task of delivering legally compliant information, served as a catalyst for his current work how a friend's article about the Foundation Capital "trillion dollor opportunity" post led to his Context Graph Manifesto his hypothesis, based on conversations with several friends at big consultancies, that the sudden interest in context graphs arose from executives reviewing their many failed 2025 AI proofs of concept his definition of a context graph: "a graph structure that is optimized for AI usage" the influence of his friend Vicky Froyen's 2019 presentation on context graphs at the first Knowledge Graph Conference the three elements he sees in a context graph - decision traces, provenance and explainability, and feedback - and the power of combining them in a single graph system their use of ontologies like PROV-O the imp...

Veronika Heimsbakk: Connecting Data Engineering and Knowledge Architecture – Episode 48

29 min

Veronika Heimsbakk With interest in knowledge graphs growing by the day, Veronika Heimsbakk is busier than ever with her efforts to connect the data engineering, information architecture, and ontology practices that drive modern knowledge engineering. Best known as an advanced knowledge graph practitioner and a leading expert on the SHACL standard, Veronika also regularly shares her knowledge through her writing, university courses, and professional workshops. We talked about: her work at Data Treehouse, creating tooling for data people to get on board the knowledge graph journey how she helps data engineers find their overlap with knowledge engineering her work to build bridges between data engineers, information architects, and ontologists how she meets data engineers on their own turf by using simple Python scripts to put their data frames into a knowledge graph how public sector compliance requirements drive demand for RDF solutions the powerful tool that helps her communicate with a variety of stakeholders and collaborators: coloring pencils how she works with information architects and enterprise architects her take on graph visualizations, that they're rarely very useful in helping her communicate with engineers and business people her approach to balance top-down ontological approaches and bottom up data engineering approaches in knowledge graph construction her early work with SHACL and her appreciation for its applicability to a wide range of use cases beyond simple data validation her take on the ongoing OWL versus SHACL discussion her preferred tool for turning modeling sketches into RDF code: WebProtégé how her work with the Norwegian maritime authorities reduced caseworker time on regulatory tasks from several weeks to a few seconds her upcoming masterclass at the Knowledge Graph Conference on transitioning from data engineering to knowledge engineering Veronika's bio Veronika Heimsbakk is a knowledge graph specialist at Data Treehou...

Joe Reis: Fighting “Context” and Other Tech-Industry Hype – Episode 47

34 min

Joe Reis When Gartner declared 2026 "The Year of Context," Joe Reis leapt into action, immediately writing a good-natured satirical article about "context products," "context lakes," and the "analyst singularity." It's a fun article that exemplifies Joe's no-nonsense approach to industry education and concludes with a serious point — "context does matter, and most organizations are terrible at it." We talked about: his forthcoming data modeling book, "Mixed Model Arts" the origins his satirical post "Gartner declares 2026 the year of context" our speculation on how the word "context" came to the fore how his decades of experience help him fine-tune his hype detectors "the one equals 10 dilemma" via which leaders extrapolate AI benefits that senior programmers gain onto less-skilled engineers the challenges that executives miss of building a semantic layer the endless quest for "silver bullets" over solving fundamental business problems the relevance of Einstein's definition of stupidity in the AI hype cycle how the big AI providers are like the ISPs of the 1990s how generative AI has accelerated and improved his workflows the trepidation around AI that he feels when he visits Silicon Valley and San Francisco the unprecedented pace and scale and context of the current AI hype cycle the role of the knowledge community in the current tech environment Joe's bio Joe Reis, a "recovering data scientist" with 20 years in the data industry, is the co-author of the best-selling O'Reilly book, "Fundamentals of Data Engineering." He’s also the instructor for the wildly popular Data Engineering Professional Certificate on Coursera, in partnership with DeepLearning.ai and AWS. Joe’s extensive experience encompasses data engineering, data architecture, machine learning, and more. He regularly keynotes major data conferences globally, advises and invests in innovative data product companies, writes at Practical Data Modeling and his personal blog and hosts the po...

Robert Sanderson: Building Yale’s Cultural Heritage Knowledge Graph – Episode 46

37 min

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 coh...

Max Gärber: Agentic AI Built on a Knowledge Graph Foundation – Episode 45

35 min

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...

Quentin Reul: Solving Business Problems with Neuro-Symbolic AI – Episode 44

29 min

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-val...

Jim Hendler: Scaling AI and Knowledge with the Semantic Web – Episode 43

54 min

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...

Brad Bolliger: Pragmatic Semantic Modeling for Government Data – Episode 42

34 min

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 ju...

Tara Raafat: Human-Centered Knowledge Graph and Metadata Leadership – Episode 41

30 min

Tara Raafat At Bloomberg, Tara Raafat applies her extensive ontology, knowledge graph, and management expertise to create a solid semantic and technical foundation for the enterprise's mission-critical data, information, and knowledge. One of the keys to the success of her knowledge graph projects is her focus on people. She of course employs the best semantic practices and embraces the latest technology, but her knack for engaging the right stakeholders and building the right kinds of teams is arguably what distinguishes her work. We talked about: her history as a knowledge practitioner and metadata strategist the serendipitous intersection of her knowledge work with the needs of new AI systems her view of a knowledge graph as the DNA of enterprise information, a blueprint for systems that manage the growth and evolution of your enterprise's knowledge the importance of human contributions to LLM-augmented ontology and knowledge graph building the people you need to engage to get a knowledge graph project off the ground: executive sponsors, skeptics, enthusiasts, and change-tolerant pioneers the five stars you need on your team to build a successful knowledge graph: ontologists, business people, subject matter experts, engineers, and a KG product owner the importance of balancing the desire for perfect solutions with the pragmatic and practical concerns that ensure business success a productive approach to integrating AI and other tech into your professional work the importance of viewing your knowledge graph as not just another database, but as the very foundation of your enterprise knowledge Tara's bio Dr. Tara Raafat is Head of Metadata and Knowledge Graph Strategy in Bloomberg’s CTO Office, where she leads the development of Bloomberg’s enterprise Knowledge Graph and semantic metadata strategy, aligning it with AI and data integration initiatives to advance next-generation financial intelligence. With over 15 years of expertise in semantic technolo...

Alexandre Bertails: The Netflix Unified Data Architecture – Episode 40

31 min

Alexandre Bertails At Netflix, Alexandre Bertails and his team have adopted the RDF standard to capture the meaning in their content in a consistent way and generate consistent representations of it for a variety of internal customers. The keys to their system are a Unified Data Architecture (UDA) and a domain modeling language, Upper, that let them quickly and efficiently share complex data projections in the formats that their internal engineering customers need. We talked about: his work at Netflix on the content engineering team, the internal operation that keeps the rest of the business running how their search for "one schema to rule them all" and the need for semantic interoperability led to the creation of the Unified Data Architecture (UDA) the components of Netflix's knowledge graph Upper, their domain modeling language their focus on conceptual RDF, resulting in a system that works more like a virtual knowledge graph his team's decision to "buy RDF" and its standards the challenges of aligning multiple internal teams on ontology-writing standards and how they led to the creation of UDA their two main goals in creating their Upper domain modeling language - to keep it as compact as possible and to support federation the unique nature of Upper and its three essential characteristics - it has to be self-describing, self-referencing, and self-governing their use of SHACL and its role in Upper how his background in computer science and formal logic and his discovery of information science brought him to the RDF world and ultimately to his current role the importance of marketing your work internally and using accessible language to describe it to your stakeholders - for example describing your work as a "domain model" rather than an ontology UDA's ability to permit the automatic distribution of semantically precise data across their business with one click how reading the introduction to the original 1999 RDF specification can help prepare yo...

Torrey Podmajersky: Aligning Language and Meaning in Complex Systems – Episode 39

32 min

Torrey Podmajersky Torrey Podmajersky is uniquely well-prepared to help digital teams align on language and meaning. Her father's interest in philosophy led her to an early intellectual journey into semantics, and her work as a UX writer at companies like Google and Microsoft has attuned her to the need to discover and convey precise meaning in complex digital experiences. This helps her span the "semantic gaps" that emerge when diverse groups of stakeholders use different language to describe similar things. We talked about: her work as president at her consultancy, Catbird Content, and as the author of two UX books how her father's interest in philosophy and semantics led her to believe that everyone routinely thinks about what things mean and how to represent meaning the role of community and collaboration in crafting the language that conveys meaning how the educational concept of "prelecting" facilitates crafting shared-meaning experiences the importance of understanding how to discern and account for implicit knowledge in experience design how she identifies "semantic gaps" in the language that various stakeholders use her discovery, and immediate fascination with, the Cyc project and its impact on her semantic design work her take on the fundamental differences between how humans and LLMs create content Torrey's bio Torrey Podmajersky helps teams solve business and customer problems using UX and content at Google, OfferUp, Microsoft, and clients of Catbird Content. She wrote Strategic Writing for UX, is co-authoring UX Skills for Business Strategy, hosts the Button Conference, and teaches content, UX, and other topics at schools and conferences in North America and Europe. Connect with Torrey online LinkedIn Catbird Content (newsletter sign-up) Torrey's Books Strategic Writing for UX UX Skills for Business Strategy Resources mentioned in this interview Cyc project Button Conference UX Methods.org Video Here’s the video version of ou...

Casey Hart: The Philosophical Foundations of Ontology Practice – Episode 38

39 min

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 T...

Chris Mungall: Collaborative Knowledge Graphs in the Life Sciences – Episode 37

32 min

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 integ...

Emeka Okoye: Exploring the Semantic Web with the Model Context Protocol – Episode 36

34 min

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...

Tom Plasterer: The Origins of FAIR Data Practices – Episode 35

32 min

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...

Mara Inglezakis Owens: A People-Loving Enterprise Architect – Episode 34

30 min

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 L...

Frank van Harmelen: Hybrid Human-Machine Intelligence for the AI Age – Episode 33

29 min

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 collabor...

Denny Vrandečić: Connecting the World’s Knowledge with Abstract Wikipedia – Episode 32

32 min

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...

Charles Ivie: The Rousing Success of the Semantic Web “Failure” – Episode 31

33 min

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 Linked...

Andrea Gioia: Human-Centered Modeling for Data Products – Episode 30

32 min

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 org...

Dave McComb: Semantic Modeling for the Data-Centric Enterprise – Episode 29

34 min

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 Enterpri...

Ole Olesen-Bagneux: Understanding Enterprise Metadata with the Meta Grid – Episode 28

34 min

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,...

Andrea Volpini: The Role of Memory in Digital Branding for AI – Episode 27

32 min

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 S...

Jacobus Geluk: Use-Case Trees for the Data-Product Marketplace – Episode 26

33 min

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 Kn...

Rebecca Schneider: Knowledge Graphs and Enterprise Content Strategy – Episode 25

32 min

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:...

Ashleigh Faith: Knowledge Graph Modeling and AI Architectures – Episode 24

33 min

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 t...

Panos Alexopoulos: Semantic Modeling for Data – Episode 23

31 min

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,...

Mike Pool: Is it time for a moratorium on the word “semantics”? – Episode 22

31 min

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/JlJjB...

Margaret Warren: Image Metadata for Knowledge Graphs and People – Episode 21

37 min

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 Cognit...

Jans Aasman: Knowledge Graphs in Modern Hybrid AI Architectures – Episode 20

37 min

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...

Juan Sequeda: LLMs as a Critical Enabler for Knowledge Graph Adoption – Episode 19

32 min

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, a...

Jesús Barrasa: Pragmatic Advice for Graph Technology Adoption – Episode 18

32 min

Jesús Barrasa Over his 20-year career, Jesús Barrasa has spanned the worlds of object-oriented property graphs and assertion-based knowledge graphs. He knows as much about these two foundational technologies as anyone and offers pragmatic advice to help architects and engineers decide which approach will work best for their needs. We talked about: his role at Neo4j in which he helps companies adopt graph technology his academic study of semantic technology and his early work on mapping relational data to ontologies and enterprise ontologies his move across the graph spectrum from RDF graphs to property graphs, culminating in his current role at Neo4j his take on the similarities and differences between RDF and property graph approaches, the key commonality being linked data and the key distinction between them being the level of abstraction that they employ different ways to approach inference the origins of the semantic web and how it has made data actionable and interoperable and smarter how the professional backgrounds of software developers can affect their choice of graph technologies the crucial role of interoperability in graph technology, and our ongoing inability to productively harness it how semantics is managed and used in the property graph and RDF worlds ontology as a technology-independent way of representing knowledge the importance of staying focused on the needs of practitioners when advising them on how to make a graph technology choice how knowledge graphs can balance the "opaque power" of large language models with "explicit, declarative, explainable power" Jesús' bio Dr. Jesús Barrasa is Neo4j's AI Field CTO and the company's resident expert in Knowledge Graphs and Semantic Technologies. He co-authored the O'Reilly book "Building Knowledge Graphs: A Practitioner's Guide" (released in July 2023) and combines over 20 years of professional experience in the data management space split between industry and research and academia....

Yaakov Belch: Humans in the Loop? No. Humans in Control – Episode 17

37 min

Yaakov Belch Yaakov Belch is an AI researcher with strong ideas about the role of humans in AI systems. Instead of "human in the loop," he argues, we should put "humans in control." Yaakov's research looks at business contracts and how knowledge graphs and AI systems can both capture their meaning more accurately and help managers make better business decisions. We talked about: his assertion that we need humans in control, not just in the loop his research on applying AI technology to business contracts, in particular the issue of resolving inconsistencies in language model results reasons to put human concerns ahead of any particular technology the importance of having humans in control when interpreting ambiguous business decisions the importance of both accounting for business intent and asking the right questions of your data and how the loop between the two tightens over time the responsibility of human users to understand how LLMs work and to prompt and otherwise interact with them accordingly why he doesn't use the term "hallucination" when talking about LLM outputs the role and implications of applying different kinds of logic in the use of knowledge graphs an analogy that shows how the concept of a Git fork can help knowledge graph engineers account in their models for different versions of reality the real-world applications of his research, especially how the practices he is exploring can create new business value the importance of building any model off of real data and always thinking about which human needs to be in control Yaakov's bio As a mathematician and data scientist, Yaakov Belch brings a unique perspective to the world of AI and knowledge graphs. With a strong background in mathematics, including participation in prestigious International Mathematical Olympiads, Yaakov went on to earn a Ph.D. in pure mathematics from the University of Cambridge. Yaakov's career has spanned both research and industry roles. He has worked as an...

Michael Iantosca: Managing Dynamic Content with Knowledge Graphs – Episode 16

34 min

Michael Iantosca Where content, knowledge management, and AI converge, you'll find Michael Iantosca. As many in the AI world flock to probabilistic models like LLMs, Michael takes a deterministic approach to content management and knowledge engineering, using ontologies and knowledge graphs to ground content in a concrete facts. This approach embodies his insight that content and the models that describe it are not static information but rather valuable, ever-evolving enterprise IP assets. We talked about: his 44-year career in content, knowledge management and localization/globalization roles the three pillars of his work: content, knowledge management, and engineering the need he sees in his work to move away from probabilistic, vector-based models to deterministic, neuro-symbolic models like knowledge graphs how he decides which models are appropriate to use with each of the varied kinds of data he works with his explorations of how to automatically construct a knowledge graph to use to power generative AI solutions how he acquires and develops ontology skills in his team how graph technology supports the "total content experiences" he builds how the non-static nature of content makes it a poor candidate to be managed in a static system like a vector-based model the relative merits and utility of 1) deterministic retrieval for structured content and 2) probabilistic retrieval for unstructured content the power of combining content models, knowledge models, and ontologies and how they can become crucial enterprise IP assets his belief that we are entering a golden age of content and knowledge engineering Michael's bio Michael Iantosca is the Senior Director of Knowledge Platforms and Engineering at Avalara, a sales tax automation company. With over four decades of leadership in technical content management, Michael has been a pioneer in advancing the profession, driving innovations in structured content, intelligent authoring, and scalable knowled...

Fran Alexander: Alien vs Predator and LLMs vs Knowledge Graphs – Episode 15

34 min

Fran Alexander When Fran Alexander looks at the current AI landscape she sees some interesting parallels between the Alien vs Predator science fiction franchise and the way RAG and other architectures are combining LLMs and knowledge graphs. We talked about: the analogy she draws between the Alien and Predator science fiction franchise with LLMs and knowledge graphs how the human-esque (if malevolent) cognitive and behavioral nature of Predators aligns more with knowledge graphs and how the unpredictable and stochastic nature of Aliens aligns more with LLMs how the eloquence of LLM outputs can deceive humans the lack of explainability and transparency in both Alien and LLM behavior, and the opposite in knowledge graphs the difficulty of dealing with baked-in biases in LLMs the lack of repeatability in LLMs and the opposite in KGs the current trend of architectures and practices like RAG that draw on the strengths of KGs and LLMs to get better results, just as the Alien and Predator media franchises combined forces how over the past year or so investment in LLMs has overshadowed all other investments, just as Aliens are out to wipe out anything that's not an Alien her approach to AI architectures that combine LLMs and knowledge graphs how different kinds of people consume LLM output how she helps enterprise decision makers choose whether to address a use case with a knowledge graph or an LLM how taxonomists and ontologists can use LLMs in their work the Alien Loves Predator UK Facebook group and Alien and Predator on a seesaw Alien and Predator cosplay actors on a seesaw Fran's bio Fran started her career as a writer and editor of dictionaries and thesauruses in the UK, and, as technology evolved, she specialised in information architecture, search systems, and digital archives, and more recently, the use of semantics in knowledge graphs and LLM applications. Having worked on reference publications including the Collins English Dictionary, and as Tax...

Andreas Blumauer: The Elements of the Enterprise Semantic Layer – Episode 14

34 min

Andreas Blumauer Every enterprise nowadays is awash in data, content, and knowledge, the understanding of which is all too often available only in employees' heads. Forward-thinking businesses are moving to knowledge graphs to capture that tacit knowledge so that they can better understand and use it. Andreas Blumauer shows how those graphs work best when they're accompanied by a domain knowledge model, creating a "semantic layer" that provides a vivid map of your business knowledge. We talked about: the recent merger of his company, Semantic Web Company, with Ontotext to form a new company, Graphwise, which aims to help enterprises build their semantic layer the 20-year-old origin story behind his definition and description of a semantic layer the emerging trend he sees of data, content, and knowledge people coming together, often around explorations of language and content structure how getting domain knowledge out of people's heads and into multimodal AI architectures can streamline business research how a semantic layer provides a map of your business knowledge the importance of a domain knowledge model in a semantic layer architecture the inadequacy of "desktop data integration," the practice of calling colleagues, consulting varied sources, and otherwise searching through ad hoc enterprise knowledge sources, and the stress it can cause how a domain knowledge model can connect and reveal knowledge across an organization the surprisingly small footprint of the domain knowledge model in an enterprise knowledge graph, typically just 1% or so of the semantic layer how LLMs can help in the discovery of data and the construction of knowledge graphs the two main elements of the semantic layer: domain knowledge models (taxonomies, ontologies, etc.) and an automatically generated enterprise knowledge graph the different perceptions of the value of the semantic layer across data and content professionals, and how the arrival of gen AI has resulted in the...

Jessica Talisman: Using SKOS to Build Better Knowledge Systems – Episode 12

35 min

Jessica Talisman Jessica Talisman is a seasoned information architect with decades of experience across a variety of domains. She's done a lot of education and outreach around her semantic and and information architecture practices. One of the most important lessons she's learned is the crucial role of standards like the W3C SKOS model to bring structure and semantics to information and knowledge systems. Since there are never enough information architects in any organization, she supports the democratization of IA practices, but she's also quick to highlight the unique skills that you can only get with deep study. We talked about: her work as a senior information architect at Adobe and previously in GLAM (galleries, libraries, art, and museums) and other domains how her work in GLAM showed her the importance of the concept of lineage and attribution and benefits of the FRBR (Functional Requirements for Bibliographic Records) framework how standards and rules bring discipline and structure to information and data ecosystems how capturing knowledge via the SKOS standard can provide on its own the structure, semantics, and disambiguation your data needs, as well as set you up for future successes the importance of focusing on semantic fundamentals and how the ensuing understanding if your data assets can improve activities like a graph RAG implementation the importance of collaborating and sharing ideas across domains democratization, evangelism, and other kinds of information architecture outreach the "Golden Spike" railroad metaphor she uses to illustrate cross-functional collaboration challenges how linked data can help span organizational silos and align stakeholders on language and terminology the importance of understanding your unique organizational fingerprint how applying the library science concept of "scholarly communications" can move organizations forward and promote innovation Jessica's bio Jessica Talisman is a Senior Information Archit...

Tony Seale: The Knowledge Graph Guy – Episode 11

33 min

Tony Seale With ten years of semantic data experience and an endless stream of insightful posts on LinkedIn, Tony Seale has earned the moniker "The Knowledge Graph Guy." Tony says there's precious little time for enterprises to prepare their data with the interconnectedness and semantic meaning that it needs to be ready for the coming wave of more powerful AI technology. We talked about: his 10-year history of applying academic knowledge graph insights to commercial work, mostly in the finance industry the yin-yang relationship in his "neuro-symbolic loop" concept that connects creative, generative LLMs and the reliable, structured knowledge provided by knowledge graphs the contrast in reasoning capabilities between LLMs and knowledge graphs how neither formal logic nor probabilistic systems are rarely the right answer on their own, hence the yin-yang analogy the crucial role of understanding and consolidating data, the gold mine on which every enterprise is sitting that describes any organization's unique value the power of understanding the "ontological core" of your business and then projecting it, selectively and strategically, to the world the urgent threat posed by snake oil salesmen and other opportunists coming into the graph world and derailing enterprises' chances to properly exploit their unique data advantage the two crucial characteristics of AI-ready data: connectedness and semantic meaning his work chairing the Data Product Ontology (DPROD) working group, an effort to provide a semantic definition of what a data product is Tony's bio For over a decade, Tony has been passionate about linking data. His creative vision for integrating Large Language Models (LLMs) and Knowledge Graphs in large organisations has gained widespread attention, particularly through his popular weekly LinkedIn posts, earning him the reputation of 'The Knowledge Graph Guy.' Tony’s journey into AI and knowledge graphs began as a secret side project, working from a...

Paco Nathan: Graph Thinking to Better Understand Graph RAG – Episode 10

41 min

Paco Nathan Graph RAG is all the rage right now in the AI world. Paco Nathan is uniquely positioned to help the industry understand and contextualize this new technology. Paco currently leads a knowledge graph practice at an AI startup, and he has been immersed in the AI community for more than 40 years. His broad and deep understanding of the tech and business terrain, along with his "graph thinking" approach, provides executives and other decision makers a clear view of terrain that is often obfuscated by less experienced and knowledgeable advisors. We talked about: his work building out the knowledge graph practice at Senzing, and their focus on entity resolution the importance of entity resolution in knowledge graph use cases like fraud detection the high percentage of knowledge graph projects that we never hear about because of their sensitive or proprietary nature his take on the concept of "graph thinking" and how he and colleagues illustrate it with a simple graph model of a medieval village how graphs add structure and context to our understanding of the world the importance of embracing complexity and the Cynefin framework in which he grounds various types of business challenges: simple, complicated, complex, and chaotic how to apply insights discerned from a Cynefin framing in management how knowledge graphs can help oranizations understand the complex environments in which they operate the wide range of industries and government entities that are applying knowledge graphs to concerns like supply chains, ESG, etc. his overview of RAG - retrieval augmented generation and graph RAG the wide variety of uses of the term "graph" in the current technology landscape Microsoft's graph RAG which uses NetworkX inside their graph RAG library, not a graph database Neo4j's approach which creates a "lexical graph" based an an NLP analysis of text "embedding graphs" ontology-based graphs Google's approach to RAG, using graph neural networks graphs...

Katariina Kari: Building Knowledge Graphs for E-Commerce Giants – Episode 9

32 min

Katariina Kari For the past eight years, Katariina Kari has built knowledge graph teams at giant e-commerce companies like IKEA and Zalando. This practical, real-world experience puts her in an elite group of ontology and knowledge graph experts. Knowledge graphs offer unique benefits to e-commerce merchants. From better product recommendations to more useful search results, the semantic capabilities that knowledge graphs provide routinely result in seven-figure sales increases. The knowledge graphs that Katariina builds provide a semantic layer in the enterprise architecture that lets companies capture, use, and re-use the organization's unique domain knowledge in any number of applications. Because knowledge graph isn't one application that does one thing. It's a paradigm shift in the way we work with data. It's a paradigm shift in the way we code, because now you don't need to put business logic into your code. We talked about: her work over the past eight years building knowledge graphs at companies like IKEA and Zalando how using knowledge graphs to improve reccomendation and search routinely brings seven-figure business benefits the set of skills and talents it takes to implement a knowledge graph project, most of which already exist in most companies how LLMs and other AI tools can help transform structured or unstructured data into semantic data, a computable resource that captures business domain knowledge some of the specific skills needed for KG work: ontology experts, back-end developers who understand the semantic web stack, data scientists and engineers, knowledge practitioners to capture domain knowledge, and product management the need in each organization for a unique knowledge graph team tailored to the needs of the company and the talent available the importance of user-centricity and use-case understanding in any knowledge graph project the benefits of capturing business logic in a semantic layer which can be used and re-used in multi...

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