Long before Foundation Capital published their "trillion dollar opportunity" article about context graphs, Daniel Davis had been building a platform for them.

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 logic and knowledge, and he settled on context graphs as the best way to do it.

https://knowledgegraphinsights.com/daniel-davis/

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."

https://knowledgegraphinsights.com/joe-reis/

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

https://knowledgegraphinsights.com/rob-sanderson/

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.

https://knowledgegraphinsights.com/max-gaerber/

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.

https://knowledgegraphinsights.com/quentin-reul/

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.

https://knowledgegraphinsights.com/jim-hendler/

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.

https://knowledgegraphinsights.com/tara-raafat/