Knowledge Graphs
Knowledge graphs represent entities and their relationships using explicit identities and meaningful relation types. They help applications connect facts across sources, query those connections and preserve provenance; their value depends on the quality of the model and evidence, rather than simply displaying data as nodes and edges.
What it is
A knowledge graph associates statements with identifiable entities, such as a component, organization or location, and expresses relationships such as manufactured by or installed in. Implementations may use RDF triples, property graphs or other representations. A schema or ontology gives relationships and types consistent meaning, while provenance connects statements to supporting records. This differs from a graph database, which is storage and query infrastructure, and from GraphRAG, which uses graph-organized evidence for generation. A knowledge graph can support rules or inference, but only under explicitly defined semantics and assumptions.
What the work involves
The practitioner designs identifiers, types and relation definitions, then maps source data into that structure. Entity resolution is audited because merging similarly named objects can create false connections. Validation checks required properties, allowed relationships and contradictions. Useful artifacts include the graph model, ingestion rules, provenance links and queries that answer actual business questions. Updating or retracting a fact should be planned alongside insertion. A graph built through model extraction needs sampled review and source traceability rather than assuming every extracted edge is reliable.
Illustrative example
A manufacturer links products to component batches, suppliers and inspection records. A query can identify products connected to a batch with a failed inspection and return the underlying records. If two suppliers share a similar trading name, their identifiers remain distinct until evidence establishes they are the same entity. An assistant can summarize the connected records, but it must distinguish a recorded failure from an inferred risk and retain the inspection source for each affected batch.
Limits and common mistakes
Graphs can organize incorrect or incomplete information very effectively. Entity ambiguity, inconsistent relationship meaning and stale updates can mislead downstream queries or generation. Absence of an edge may mean missing data rather than evidence that a relationship does not exist. Quality checks need semantic and provenance review as well as schema validation. Graph modeling is worthwhile when explicit connections answer important questions; a graph representation alone does not create knowledge or guarantee valid inference.
Prerequisites
GraphRAG augments vector-based RAG with structured graph traversal — you must understand what basic RAG does to extend it
GNN concepts (message passing, node embeddings) inform how knowledge graphs can be enriched, though not strictly required
Related skills
- → is part of: GraphRAG
- ← is an instance of: Neo4j
- ← is subcategory of: Graph Databases
Sources and further reading
- RDF 1.1 Concepts and Abstract Syntax
Defines a standard graph data model with identified resources and statements.
- Get started with Neo4j
Provides an official property-graph implementation perspective.
Last updated: 2026-10-10