Knowledge Graph

Knowledge graphs represent information as subject-predicate-object triples (e.g., "Acme Corp — supplies — Widget A", "Widget A — hasSpec — ISO-9001"), forming a richly interconnected semantic network. Unlike relational databases optimized for structured tabular data or document stores optimized for unstructured text, knowledge graphs excel at representing heterogeneous entities with varying attributes and complex many-to-many relationships. This structure makes traversal queries — "find all suppliers of components used in products sold to customers in the EU" — natural and efficient.

Enterprise knowledge graphs integrate data from multiple source systems: CRM platforms, ERP systems, product databases, regulatory filings, and unstructured documents. Graph construction pipelines extract entities and relationships from these sources using a combination of rule-based extraction, NLP entity linking, and increasingly LLM-based information extraction. The resulting graph is stored in a native graph database (Neo4j, Amazon Neptune, TigerGraph) or an RDF triplestore (Stardog, Apache Jena) and exposed via graph query languages such as Cypher, Gremlin, or SPARQL.

The primary enterprise use cases for knowledge graphs include enterprise search and data discovery (surfacing related entities alongside search results), data integration and master data management (resolving the same real-world entity across multiple source systems), compliance and lineage tracking (tracing data provenance through complex processing pipelines), and increasingly as a structured knowledge source for AI reasoning. Knowledge graphs provide the kind of verifiable, auditable factual structure that complements the broad but unreliable world knowledge of large language models.