Knowledge Graph

A knowledge graph is the structured representation of entities and the relationships between them as a graph — nodes for entities (people, organizations, products, places, concepts), edges for relationships (works-at, manufactured-by, located-in, subClass-of) — that has become foundational infrastructure for grounding LLMs, powering search and recommendations, and enforcing domain-specific reasoning. The discipline predates AI by decades, with roots in semantic networks, frame systems, and the Semantic Web vision; the modern enterprise revival was catalyzed by Google's 2012 Knowledge Graph announcement and the subsequent enterprise adoption at Amazon, Microsoft, eBay, LinkedIn, Airbnb, and Uber. Production knowledge-graph platforms include Neo4j (the dominant property-graph database), TigerGraph, Amazon Neptune, Azure Cosmos DB Gremlin, ArangoDB (multi-model), Stardog (RDF-based), GraphDB by Ontotext, and the open-source camp led by Apache Jena, RDF4J, and Blazegraph. The two dominant data models are property graphs (nodes and edges with key-value properties, queried with Cypher or Gremlin) and RDF graphs (triples of subject-predicate-object, queried with SPARQL, supporting ontology reasoning). For AI specifically, knowledge graphs have re-emerged as a complement to vector-based RAG in the pattern called GraphRAG — Microsoft Research's 2024 paper showed that combining graph-traversal-based retrieval with LLM generation outperforms pure vector RAG on multi-hop reasoning tasks. Practical recipe with Neo4j: install Neo4j Desktop or use AuraDB cloud, model your domain with Cypher (CREATE (a:Person {name:'Alice'})-[:WORKS_AT]->(c:Company {name:'Acme'})), expose the graph via Cypher queries or the LangChain/LlamaIndex GraphCypherQAChain that lets an LLM translate natural-language questions into Cypher. AI governance teams use knowledge graphs to enforce domain consistency (the LLM can be constrained to only return facts present in the graph), to provide explainable provenance (every answer traces back to graph edges with citations), and to model access control at the entity level.

Knowledge graphs as the natural successor to 25 years of taxonomy work: Centralpoint has maintained client taxonomies, audience graphs, and entity hierarchies for 25 years — that structured knowledge is exactly the substrate modern knowledge graphs operate on. The 25-year discipline of curating relationships, hierarchies, and audience entitlements pays off directly in graph-based grounding. Graphs run on-premise, tokens meter per skill, and graph-grounded chatbots deploy through one line of JavaScript.


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