research-wiki

Knowledge graph (typed-edge)

A representation of a knowledge base as entities connected by typed edges, as opposed to (or alongside) a flat collection of pages or vector chunks. Introduced to this wiki by gbrain, whose “self-wiring knowledge graph” is its distinguishing feature.

How GBrain does it

• Every page write extracts entity references from markdown wikilink / typed-link syntax and creates edges with zero LLM calls — pure pattern matching.
• Edges are typed: attended, works_at, invested_in, founded, advises, mentions, … Multi-hop traversal answers questions like “who works at Acme?” or “what did Bob invest in this quarter?” that vector search alone cannot reach.
• Framed as: “Vector search returns chunks that are semantically close. The graph returns chunks that are factually connected.” The graph is credited with GBrain’s +31.4-point P@5 lift over vector-only RAG (see retrieval-augmented-generation).

Relation to the rest of the wiki

This is a mechanized, scaled realization of associative-trails — Bush’s hand-built named trails (memex) become automatically-extracted typed edges. It is also what most distinguishes gbrain from the index-only llm-wiki pattern and from a pure-search tool like qmd.

Another build strategy (llm-wiki-agent)

llm-wiki-agent builds its graph in two passes: a deterministic pass turns every wikilink into an EXTRACTED edge, then a semantic pass has the agent infer implicit relationships as INFERRED (scored) or AMBIGUOUS edges. That sits between GBrain’s zero-LLM typed extraction and fully manual linking.

Adding time — the temporal turn (temporal-knowledge-graph)

agent-memory-knowledge-graphs extends the typed graph with a time axis for persistent agent memory: a temporal-knowledge-graph records when each fact was true and when it was learned (bi-temporal modeling), so changing facts are time-bounded rather than overwritten. Built incrementally with graphiti (Zep) over Neo4j. This closes a gap neither GBrain’s static typed edges nor vector search address — temporal validity (which of two conflicting facts is true now) — and is framed as graphs “quietly replacing RAG” for agent systems.

A hand-curated forebear (roam-research)

roam-research made the personal knowledge graph mainstream — notes as nodes, bidirectional links as edges, with a Graph Overview. It’s the manual version of what gbrain auto-wires; its “Unlinked References” hint at the automated association the LLM systems deliver.

The formal schema layer — ontologies

A knowledge graph is built on top of an ontology: the ontology defines which concept types and relationship types exist in a domain (the grammar); the graph populates those slots with actual entities and facts (the data). ontologies-knowledge-graphs-ai (Mysore) shows LLMs can now draft ontology schemas from prose and then populate the graph in a constrained second pass — the schema limits what assertions the model can make, reducing hallucination. Same containment logic as agent-guardrails, applied to knowledge extraction instead of agent actions.

Related

gbrain · temporal-knowledge-graph · graphiti · agent-memory-knowledge-graphs · llm-wiki-agent · roam-research · associative-trails · retrieval-augmented-generation · memex · ontology · ontologies-knowledge-graphs-ai