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This paper studies the complexity of answering two-way (conjunctive) regular path queries over knowledge bases with linear and guarded existential rules, establishing completeness results for data and combined complexity.
A CIO shares observations on the challenges of applying semantic layers and ontologies to enterprise AI agents, highlighting the need for dynamic context selection, continuous evolution of institutional knowledge, and representing judgment rather than just static facts.
The author shares five mistakes made while building a unified memory layer for AI agents using knowledge graphs and ontologies, emphasizing that agent memory is a data-modeling problem rather than a retrieval problem.
The article shares decks from the Knowledge Graph Conference (KGC) 2026, highlighting a trend of enterprises deploying production-grade knowledge graphs for reasoning and governance rather than just vector retrieval.