Whether IT leaders opt for the precision of a Knowledge Graph or the efficiency of a Vector DB, the goal remains clear—to harness the power of RAG systems and drive innovation, productivity, and ...

Wikidata has built the semantic web backbone supporting knowledge cards in popular engines. Now, it's extending this foundation using a vector database to enhance its existing knowledge graph and ...

There’s been a debate of sorts in AI circles about which database is more important in finding truthful information in generative AI applications: graph or vector databases. AWS decided to leave the ...

As AI systems evolve from assistants into autonomous collaborators, enterprises will need durable memory, explicit semantics, lineage, governance, and explainability. AllegroGraph and GraphTalker ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

TigerGraph, the enterprise AI infrastructure and graph database leader, is releasing its next generation graph and vector hybrid search, delivering the industry's “most advanced” solution for ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search ...