As AI unleashes a host of new tools and agents to help engineers build knowledge graphs, engineers like François Scharffe are tasked with harnessing these new capabilities and deploying them effectively.

François brings decades of AI experience to the goal he set for himself earlier this year: to automate his own job. He's making good progress, but he's also finding that there's still plenty of need for him and other humans in the workflows he's designing.

https://knowledgegraphinsights.com/francois-scharffe/

When the idea of the semantic spectrum was introduced 25 years ago, it was meant to be a conceptual tool to help programmers and architects understand the relative benefits of different levels of semantic effort.

Now, a couple of decades later, Jessica Talisman has repurposed the model as a way to organize the actual process of developing enterprise knowledge infrastructure, which she shares in her new book, "Ontology Pipeline: A Framework for Knowledge Engineering."

https://knowledgegraphinsights.com/jessica-2/

Knowledge graphs are complicated. They might entail multiple ontologies, several data sources, and any number of generated graphs and other context-establishing elements.

Eric Little came up with the idea of "super domains" to understand and manage these important but ephemeral pieces of an enterprise's semantic knowledge architecture. A super domain might establish context that holds for just a few seconds for an ATM transaction or for several years for a clinical trial.

https://knowledgegraphinsights.com/eric-little/

Most conversations about taming generative AI focus on guardrails bolted on after the fact. Dougal Watt takes a different approach: generate the meaning first, as an ontology, and let everything else — agents, APIs, knowledge graphs — flow from that.

His standing-room-only workshop on AI-augmented ontology creation at the Knowledge Graph Conference drew both business executives and semantic engineers, highlighting the broad interest in the need for accurate, trustworthy AI.

https://knowledgegraphinsights.com/dougal-watt/

The emerging field of neurosymbolic AI — combining LLMs and knowledge graphs in hybrid AI architectures — is new to a lot of people.

For Yann Le Franc, it's the story of his career — from his academic days as a computational neuroscientist to his current work creating enterprise systems that ground LLMs in ontology-backed knowledge graphs.

Over his extensive research and consulting career, Yann has honed his ontology design process, which he's now sharing in a platform that guides others in applying those time-tested ontology practices.

https://knowledgegraphinsights.com/yann-le-franc/

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/

As the Cyc project winds down, one of its main contributors, Benjamin Rode, is attempting to migrate as much as possible of its upper ontology to RDF. It's an ambitious project, much like Cyc itself.

Cyc arose from the early AI research community, and Ben has a fascinating perspective on that milieu as well.

https://knowledgegraphinsights.com/ben-rode/

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/