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Casey Hart: The Philosophical Foundations of Ontology Practice – Episode 38
Casey Hart Ontology engineering has its roots in the idea of ontology as defined by classical philosophers. Casey Hart sees many other connections between professional ontology practice and the academic discipline of philosophy and shows how concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology in general. We talked about: his work as a lead ontologist at Ford and as an ontology consultant his academic background in philosophy the variety of pathways into ontology practice the philosophical principles like metaphysics, epistemology, and logic that inform the practice of ontology his history with the the Cyc project and employment at Cycorp how he re-uses classes like "category" and similar concepts from upper ontologies like gist his definition of "AI" - including his assertion that we should use term to talk about a practice, not a particular technology his reminder that ontologies are models and like all models can oversimplify reality Casey's bio Casey Hart is the lead ontologist for Ford, runs an ontology consultancy, and pilots a growing YouTube channel. He is enthusiastic about philosophy and ontology evangelism. After earning his PhD in philosophy from the University of Wisconsin-Madison (specializing in epistemology and the philosophy of science), he found himself in the private sector at Cycorp. Along his professional career, he has worked in several domains: healthcare, oil & gas, automotive, climate science, agriculture, and retail, among others. Casey believes strongly that ontology should be fun, accessible, resemble what is being modelled, and just as complex as it needs to be. He lives in the Pacific Northwest with his wife and three daughters and a few farm animals. Connect with Casey online LinkedIn ontologyexplained at gmail dot com Ontology Explained YouTube channel Video Here’s the video version of our conversation: https://youtu.be/siqwNncPPBw Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 38. When the subject of philosophy comes up in relation to ontology practice, it's typically cited as the origin of the term, and then the subject is dropped. Casey Hart sees many other connections between ontology practice and it its philosophical roots. In addition to logic as the foundation of OWL, he shows how philosophy concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology in general. Interview transcript Larry: Hi, everyone. Welcome to episode number 38 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Casey Hart. Casey has a really cool YouTube channel on the philosophy behind ontology engineering and ontology practice. Casey is currently an ontologist at Ford, the motor car company. So welcome Casey, tell the folks a little bit more about what you're up to these days. Casey: Hi. Thanks, Larry. I'm super excited to be here. I've listened to the podcast, and man, your intro sounds so smooth. I was like, "I wonder how many edits that takes." No, you just fire them off, that's beautiful. Casey: Yeah, so like you said, these days I'm the ontologist at Ford, so building out data models for sensor data and vehicle information, all those sorts of fun things. I am also working as a consultant. I've got a couple of different startup healthcare companies and some cybersecurity stuff, little things around the edge. I love evangelizing ontology, talking about it and thinking about it. And as you mentioned for the YouTube channel, that's been my creative outlet. My background is in philosophy and I was interested in, I got my PhD in philosophy, I was going to teach it. You write lots of papers, those sorts of things, and I miss that to some extent getting out into industry, and that's been my way back in to, all right, come up with an idea,
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Chris Mungall: Collaborative Knowledge Graphs in the Life Sciences – Episode 37
Chris Mungall Capturing knowledge in the life sciences is a huge undertaking. The scope of the field extends from the atomic level up to planetary-scale ecosystems, and a wide variety of disciplines collaborate on the research. Chris Mungall and his colleagues at the Berkeley Lab tackle this knowledge-management challenge with well-honed collaborative methods and AI-augmented computational tooling that streamlines the organization of these precious scientific discoveries. We talked about: his biosciences and genetics work at the Berkeley Lab how the complexity and the volume of biological data he works with led to his use of knowledge graphs his early background in AI his contributions to the gene ontology the unique role of bio-curators, non-semantic-tech biologists, in the biological ontology community the diverse range of collaborators involved in building knowledge graphs in the life sciences the variety of collaborative working styles that groups of bio-creators and ontologists have created some key lessons learned in his long history of working on large-scale, collaborative ontologies, key among them, meeting people where they are some of the facilitation methods used in his work, tools like GitHub, for example his group's decision early on to commit to version tracking, making change-tracking an entity in their technical infrastructure how he surfaces and manages the tacit assumptions that diverse collaborators bring to ontology projects how he's using AI and agentic technology in his ontology practice how their decision to adopt versioning early on has enabled them to more easily develop benchmarks and evaluations some of the successes he's had using AI in his knowledge graph work, for example, code refactoring, provenance tracking, and repairing broken links Chris's bio Chris Mungall is Department Head of Biosystems Data Science at Lawrence Berkeley National Laboratory. His research interests center around the capture, computational integration, and dissemination of biological research data, and the development of methods for using this data to elucidate biological mechanisms underpinning the health of humans and of the planet. He is particularly interested in developing and applying knowledge-based AI methods, particularly Knowledge Graphs (KGs) as an approach for integrating and reasoning over multiple types of data. Dr. Mungall and his team have led the creation of key biological ontologies for the integration of resources covering gene function, anatomy, phenotypes and the environment. He is a principal investigator on major projects such as the Gene Ontology (GO) Consortium, the Monarch Initiative, the NCATS Biomedical Data Translator, and the National Microbiome Data Collaborative project. Connect with Chris online LinkedIn Berkeley Lab Video Here’s the video version of our conversation: https://youtu.be/HMXKFQgjo5E Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 37. The span of the life sciences extends from the atomic level up to planetary ecosystems. Combine this scale and complexity with the variety of collaborators who manage information about the field, and you end up with a huge knowledge-management challenge. Chris Mungall and his colleagues have developed collaborative methods and computational tooling that enable the construction of ontologies and knowledge graphs that capture this crucial scientific knowledge. Interview transcript Larry: Hi everyone. Welcome to episode number 37 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Chris Mungall. Chris is a computational scientist working in the biosciences at the Lawrence Berkeley National Laboratory. Many people just call it the Berkeley Lab. He's the principal investigator in a group there, has his own lab working on a bunch of interesting stuff, which we're going to talk about today.
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Emeka Okoye: Exploring the Semantic Web with the Model Context Protocol – Episode 36
Emeka Okoye Semantic technologies permit powerful connections across a variety of linked data resources across the web. Until recently, developers had to learn the RDF language to discover and use these resources. Leveraging the new Model Context Protocol (MCP) and LLM-powered natural-language interfaces, Emeka Okoye has created the RDF Explorer, an MCP service that lets any developer surf the semantic web without having to learn its specialized language. We talked about: his long history in knowledge engineering and AI agents his deep involvement in the business and technology communities in Nigeria, including founding the country's first internet startup how he was building knowledge graphs before Google coined the term an overview of MCP, the Model Context Protocol, and its benefits the RDF Explorer MCP server he has developed how the MCP protocol and helps ease some of the challenges that semantic web developers have traditionally faced the capabilities of his RDF Explorer: facilitating communication between AI applications, language models, and RDF data enabling graph exploration and graph data analysis via SPARQL queries browsing, accessing, and evaluating linked-open-data RDF resources the origins of RDF Explorer in his attempt to improve ontology engineering tooling his objections to "vibe ontology" creation the ability of RDF Explorer to let non-RDF developers users access knowledge graph data how accessing knowledge graph data addresses the problem of the static nature of the data in language models the natural connections he sees between neural network AI and symbolic AI like knowledge graphs, and the tech tribalism he sees in the broader AI world that prevents others from seeing them how the ability of LLMs to predict likely language isn't true intelligence or actual knowledge some of the lessons he learned by building the RDF Explorer, e.g., how the MCP protocol removes a lot of the complexity in building hybrid AI solutions how MCP helps him validate the ontologies he creates Emeka's bio Emeka is a Knowledge Engineer, Semantic Architect, and Generative AI Engineer who leverages his over two decades of expertise in ontology and knowledge engineering and software development to architect, develop, and deploy innovative, data-centric AI products and intelligent cognitive systems to enable organizations in their Digital Transformation journey to enhance their data infrastructure, harness their data assets for high-level cognitive tasks and decision-making processes, and drive innovation and efficiency enroute to achieving their organizational goals. Emeka’s experience has embraced a breadth of technologies his primary focus being solution design, engineering and product development while working with a cross section of professionals across various cultures in Africa and Europe in solving problems at a complex level. Emeka can understand and explain technologies from deep diving under the hood to the value proposition level. Connect with Emeka online LinkedIn Making Knowledge Graphs Accessible: My Journey with MCP and RDF Explorer RDF Explorer (GitHub) Video Here’s the video version of our conversation: https://youtu.be/GK4cqtgYRfA Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 36. The widespread adoption of semantic technologies has created a variety of linked data resources on the web. Until recently, you had to learn semantic tools to access that data. The arrival of LLMs, with their conversational interfaces and ability to translate natural language into knowledge graph queries, combined with the new Model Context Protocol, has empowered semantic web experts like Emeka Okoye to build tools that let any developer surf the semantic web. Interview transcript Larry: Hi, everyone. Welcome to episode number 36 of the Knowledge Graph Insights podcast.
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Tom Plasterer: The Origins of FAIR Data Practices – Episode 35
Tom Plasterer Shortly after the semantic web was introduced, the demand for discoverable and shareable data arose in both research and industry. Tom Plasterer was instrumental in the early conception and creation of the FAIR data principle, the idea that data should be findable, accessible, interoperable, and reusable. From its origins in the semantic web community, scientific research, and the pharmaceutical industry, the FAIR data idea has spread across academia, research, industry, and enterprises of all kinds. We talked about: his recent move from a big pharma company to Exponential Data where he leads the knowledge graph and FAIR data practices the direct line from the original semantic web concept to FAIR data principles the scope of the FAIR acronym, not just four concepts, but actually 15 how the accessibility requirement in FAIR distinguishes the standard from the open data the role of knowledge graphs in the implementation of a FAIR data program the intentional omission of prescribed implementations in the development of FAIR and the ensuing variety of implementation patterns how the desire for consensus in the biology community smoothed the development of the FAIR standard the role of knowledge graphs in providing a structure for sharing terminology and other information in a scientific community how his interest in omics led him to computer science and then to the people skills crucial to knowledge graph work the origins of the impetus for FAIR in European scientific research and the pharmaceutical industry the growing adoption of FAIR as enterprises mature their web thinking and vendors offer products to help with implementations the roles of both open science and the accessibility needs in industry contributed to the development of FAIR the interesting new space at the intersection of generative AI and FAIR and knowledge graph the crucial foundational role of FAIR in AI systems Tom's bio Dr. Tom Plasterer is a leading expert in data strategy and bioinformatics, specializing in the application of knowledge graphs and FAIR data principles within life sciences and healthcare. With over two decades of experience in both industry and academia, he has significantly contributed to bioinformatics, systems biology, biomarker discovery, and data stewardship. His entrepreneurial ventures include co-founding PanGenX, a Personalized Medicine/Pharmacogenetics Knowledge Base start-up, and directing Project Planning and Data Interpretation at BG Medicine. During his extensive tenure at AstraZeneca, he was instrumental in championing Data Centricity, FAIR Data, and Knowledge Graph initiatives across various IT and scientific business units. Currently, Dr. Plasterer serves as the Managing Director of Knowledge Graph and FAIR Data Capability at XponentL Data, where he defines strategy and implements advanced applications of FAIR data, knowledge graphs, and generative AI for the life science and healthcare industries. He is also a prominent figure in the community, having co-founded the Pistoia Alliance FAIR Data Implementation group and serving on its FAIR data advisory board. Additionally, he co-organizes the Health Care and Life Sciences symposium at the Knowledge Graph Conference and is a member of Elsevier’s Corporate Advisory Board. Connect with Tom online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/Lt9Dc0Jvr4c Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 35. With the introduction of semantic web technologies in the early 2000s, the World Wide Web began to look something like a giant database. And with great data, comes great responsibility. In response to the needs of data stewards and consumers across science, industry, and technology, the FAIR data principle - F A I R - was introduced. Tom Plasterer was instrumental in the early efforts to make web data findable,
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Mara Inglezakis Owens: A People-Loving Enterprise Architect – Episode 34
Mara Inglezakis Owens Mara Inglezakis Owens brings a human-centered focus to her work as an enterprise architect at a major US airline. Drawing on her background in the humanities and her pragmatic approach to business, she has developed a practice that embodies both "digital anthropology" and product thinking. The result is a knowledge architecture that works for its users and consistently demonstrates its value to key stakeholders. We talked about: her role as an enterprise architect at a major US airline how her background as a humanities scholar, and especially as a rhetoric teacher, prepared her for her current work as a trusted business advisor some important mentoring she received early in her career how "digital anthropology" and product thinking fit into her enterprise architecture practice how she demonstrates the financial value of her work to executives and other stakeholders her thoughtful approach to the digitalization process and systems design the importance of documentation in knowledge engineering work how to sort out and document stakeholders' self-reports versus their actual behavior the scope of her knowledge modeling work, not just physical objects in the world, but also processes and procedures two important lessons she's learned over her career: don't be afraid to justify financial investment in your work, and "don't be so attached to an ideal outcome that you miss the best possible" Mara's bio Mara Inglezakis Owens is an enterprise architect who specializes in digitalization and knowledge management. She has deep experience in end-to-end supply chain as well as in planning, product, and program management. Mara’s background is in epistemology (history and philosophy of science, information science, and literature), which gives a unique, humanistic flavor to her practice. When she is not working, Mara enjoys aviation, creative writing, gardening, and raising her children. She lives in Minneapolis. Connect with Mara online LinkedIn email: mara dot inglezakis dot owens at gmail dot com Video Here’s the video version of our conversation: https://youtu.be/d8JUkq8bMIc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 34. When think about architecting knowledge systems for a giant business like a global airline, you might picture huge databases and complex spaghetti diagrams of enterprise architectures. These do in fact exist, but the thing that actually makes these systems work is an understanding of the needs of the people who use, manage, and finance them. That's the important, human-focused work that Mara Inglezakis Owens does as an enterprise architect at a major US airline. Interview transcript Larry: Hi, everyone. Welcome to episode 34 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, Mara, I'm going to get this right, Inglezakis Owens. She's an enterprise architect at a major US airline. So, welcome, Mara. Tell the folks a little bit more about what you're up to these days. Mara: Hi, everybody. My name's Mara. And these days I am achieving my childhood dream of working in aviation, not as a pilot, but that'll happen, but as an enterprise architect. I've been doing EA, also data and information architecture, across the whole scope of supply chain for about 10 years, everything from commodity sourcing to SaaS, software as a service, to now logistics. And a lot of my days, I spend interviewing subject matter experts, convincing business leaders they should do stuff, and on my best days, I get to crawl around on my hands and knees in an airplane hangar. Larry: Oh, fun. That is ... Yeah. I didn't know ... I knew that there's that great picture of you sitting in the jet engine, but I didn't realize this was the fulfillment of a childhood dream. That's awesome. But everything you've just said ties in so well to the tagline on your LinkedIn pro...
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