Entity Relation Graphs
Entity relation graphs are structured data representations that map entities and their relationships as nodes and edges within a graph database. Unlike vector databases that rely on semantic embeddings and similarity-based retrieval, entity relation graphs store knowledge through explicit, queryable connections between discrete entities. This deterministic approach allows for precise navigation of relationships and supports complex queries that traverse multiple connection points.
Structure and Implementation
Entity relation graphs organize information by representing real-world objects, concepts, or people as nodes, while edges define how these entities connect and interact. Each edge typically carries metadata describing the nature of the relationship—such as “authored,” “located in,” or “depends on.” This explicit structure makes the graph’s logic transparent and auditable, in contrast to the implicit associations learned by machine learning models.
Use in Graph RAG
Entity relation graphs serve as a foundation for Graph Retrieval Augmented Generation (Graph RAG), where they enable large language models to retrieve relevant information by traversing relationships rather than relying solely on semantic similarity. During retrieval, queries can follow specific relationship paths through the graph to surface contextually appropriate facts, reducing hallucinations and improving answer grounding. The structured nature of entity relation graphs makes it possible to combine retrieved facts with explicit reasoning about how they connect.