A common approach to combining an ontology with vector search is to use a hybrid retrieval architecture that integrates semantic retrieval with graph-based knowledge representation. In this pattern, entities, concepts, documents, or text chunks are indexed using vector embeddings to support semantic search. When a user submits a query, vector search retrieves the most semantically relevant results based on meaning rather than exact keyword matches.
The retrieved results can then be used as entry points into a knowledge graph or ontology, where graph traversal is performed to discover related entities, hierarchical relationships, attributes, and additional contextual information. Depending on the system design, graph exploration may also be used to expand, filter, or re-rank the results returned by vector search.
A typical workflow includes:
- Creating vector embeddings for documents, entities, concepts, or other searchable content.
- Using vector search to identify semantically relevant results.
- Traversing the ontology or knowledge graph to retrieve connected concepts, relationships, metadata, and supporting context.
- Combining information from both sources to generate a final response or ranked result set.
This approach is particularly useful in Retrieval-Augmented Generation (RAG) systems because vector search and ontologies address different aspects of retrieval. Vector search excels at finding content with similar meaning, while ontologies provide explicit domain knowledge through defined relationships, hierarchies, classifications, and constraints.
For example, vector search may identify a relevant entity or document based on semantic similarity. The ontology can then be used to discover related concepts, parent-child relationships, associated entities, or domain-specific connections that are not captured by embedding similarity alone. By combining semantic retrieval with structured knowledge, organizations can improve retrieval accuracy, explainability, and contextual relevance, especially for queries that depend on understanding relationships between concepts.