Building More Expressive Next-Gen Knowledge Resources
KGC 2025
•
33m
Mike Dillinger, hypergraf, Chief Scientist
The need for structured knowledge -- in the form of taxonomies, ontologies, and knowledge graphs -- has never been more urgent. It is crucial for evolving gen AI to next-gen AI.
But one of the blockers for deeper investment and broader deployment is the diversity of concepts and options that we have developed.
This talk describes how we alleviate this problem by showing that each kind of structured knowledge makes a unique contribution to the expressivity of next-gen knowledge representations. In our view, the usual options are neither complete in themselves nor mutually exclusive but form a cumulative scale of increasing expressivity. We draw on examples from LinkedIn to show how greater expressivity leads to more applications.
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