GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization. Making your brand machine-readable and increasing its chances of being ...

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

The ordinary graphite in pencil lead is proving to be surprisingly multifaceted at the microscale. In a study published in ...

Abstract: In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs ...

Retrieval-augmented generation (RAG) has emerged as a pivotal framework in AI, significantly enhancing the accuracy and relevance of responses generated by large language models (LLMs) leveraging ...

State of the art in the graph-vector hybrid era of agent memory. 4-pillar cognitive model, Neo4j graph storage, HNSW vector indices. 81.3% on LongMemEval. The only published benchmark comparison where ...

Magic-angle twisted bilayer graphene (MATBG) is a material created by stacking two sheets of graphene onto each other, with a small twist angle of about 1.1°. At this "magic angle," electrons move ...

Abstract: Knowledge Graphs (KGs), with their intricate hierarchies and semantic relationships, present unique challenges for graph representation learning, necessitating tailored approaches to ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Erik Steiger discusses the operational pain ...

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge ...