Knowledge Graph Insights

Alexandre Bertails, a software engineer at Netflix's content engineering team, talks about pioneering the Unified Data Architecture (UDA) to enhance semantic interoperability through RDF. He explores the challenge of creating a singular schema for diverse internal needs while detailing the innovative Upper domain modeling language. Bertails highlights Upper's self-describing and self-governing traits, its compact design, and how Netflix operationalized these concepts to improve data management and accessibility across teams. Tune in for insights on making complex data relatable!

Torrey Podmajersky, a seasoned UX/content strategist and president of Catbird Content, dives into the intricate world of semantics in design. Influenced by her father's philosophical pursuits, she explores how language shapes user experiences. Torrey highlights the importance of prelecting to bridge implicit knowledge gaps and the need for cross-functional teamwork in crafting effective product language. She also discusses the Cyc project and contrasts the decision-making processes of human writers with LLMs, revealing the profound impact of semantic design.

Casey Hart, Lead Ontologist for Ford and ontology consultant, blends a rich background in philosophy with practical ontology applications. He explores the philosophical foundations of ontology, revealing how concepts like metaphysics and epistemology shape knowledge graphs and AI. He shares insights from his time at Cycorp, discusses the complexities of defining AI beyond technology, and warns against inflated expectations surrounding AI in Silicon Valley. Hart emphasizes that ontologies are models that can oversimplify reality, making philosophical engagement crucial.

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

Emeka Okoye, a seasoned Knowledge Engineer and Semantic Architect with over 20 years in knowledge engineering, dives into the world of the Semantic Web. He shares insights on the transformative Model Context Protocol (MCP) and its impact on AI applications. Emeka discusses his RDF Explorer, a tool that allows developers easy access to semantic data without specialized language skills. The conversation also touches on the evolution of ontology engineering, his history in tech innovation in Nigeria, and the importance of making semantic technologies accessible globally.

In this discussion, Tom Plasterer, Managing Director at XponentL Data and a leading expert in data strategy, delves into the origins and significance of FAIR data principles. He unpacks how the concept evolved from the semantic web to address the need for discoverable data in research and industry. Tom reveals the 15 facets of the FAIR acronym and emphasizes the critical role of knowledge graphs in implementing these standards. His journey from bioinformatics to data management showcases the importance of collaboration and shared terminology in enhancing data practices, especially in pharmaceuticals and life sciences.

Mara Inglezakis Owens, an enterprise architect at Delta Air Lines, blends her humanities background with digital anthropology to shape user-focused architecture. She discusses how mentoring shaped her approach and emphasizes the need for understanding actual stakeholder behaviors over self-reports. Mara also shares insights on justifying financial investments in her work, the significance of documentation in knowledge engineering, and lessons learned about embracing imperfection in digital systems design. Her human-centered focus exemplifies the evolution of enterprise architecture in modern businesses.

Frank van Harmelen, a leading AI professor at Vrije Universiteit in Amsterdam, discusses the integration of human and machine intelligence. He emphasizes the importance of hybrid collaboration, advocating for AI systems that enhance rather than replace human capabilities. Topics include the emergence of neuro-symbolic systems, the evolution of conversational interfaces, and the challenges of managing interdisciplinary research teams. He also highlights innovative applications of AI in healthcare and the need for a shared worldview to foster effective collaboration.

Join Denny Vrandečić, Head of Special Projects at the Wikimedia Foundation and founder of Wikidata, as he discusses the groundbreaking Abstract Wikipedia initiative. He shares insights on how it aims to democratize knowledge sharing by allowing contributions in any language. Denny reflects on his journey from the creation of Wikidata to exploring how Abstract Wikipedia can enhance multilingual knowledge accessibility. He also dives into the potential of community collaboration and the use of language models to create structured knowledge representations.

Charles Ivie, a Senior Graph Architect at Amazon Web Services with over 15 years in the knowledge graph community, debunks the myth that the semantic web has failed. He argues it's a 'catastrophically successful failure' with over half of the web utilizing RDF annotations. The discussion explores how RDF serves as a Rosetta Stone for knowledge representation, enabling better communication and innovative solutions. Ivie emphasizes the importance of domain-specific ontologies and the growing adoption of knowledge graphs in enterprises, showcasing their transformative potential.