Knowledge Graph
A structured representation of entities, relationships, and properties that machines can traverse and reason over.
What is Knowledge Graph?
A knowledge graph encodes facts as triples (entity–relationship–entity) and enables multi-hop queries that pure vector search cannot answer reliably. In enterprise AI, knowledge graphs power policy applicability checks, supply-chain tracing, and contract-clause navigation. See Knowledge Graph RAG and Federated Knowledge Graph Playbook.
What is an example of Knowledge Graph?
A supply-chain graph links suppliers, components, factories, contracts, countries, and incidents. An agent can trace which products depend on a sanctioned sub-supplier and cite every relationship used.
How is Knowledge Graph different from related concepts?
A relational database stores structured tables optimized for transactions and queries. A knowledge graph emphasizes semantic relationships and flexible traversal; organizations often use both.
What should enterprises evaluate for Knowledge Graph?
- Start with questions that require relationships, not with an attempt to model the entire organization.
- Define entity identity, ontology ownership, provenance, update rules, and permission behavior.
- Evaluate graph retrieval on multi-hop accuracy and evidence paths, then combine it with text retrieval where needed.
Read the full guide: Knowledge Graph — in-depth article →
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