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.

https://knowledgegraphinsights.com/alex-bertails/

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.

https://knowledgegraphinsights.com/torrey/

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.

https://knowledgegraphinsights.com/casey-hart/

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.

https://knowledgegraphinsights.com/chris-mungall/

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.

https://knowledgegraphinsights.com/emeka-okoye/

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.

https://knowledgegraphinsights.com/tom-plasterer/

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.

https://knowledgegraphinsights.com/mara-owens/

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.

https://knowledgegraphinsights.com/frank-van-harmelen/