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.

https://knowledgegraphinsights.com/lulit-tesfaye/

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."

https://knowledgegraphinsights.com/giancarlo-guizzardi/

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.

https://knowledgegraphinsights.com/rdf12/

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/