By JAKALA
Discover how ontology-aware retrieval strategies enable Large Language Model–based systems to identify relevant concepts accurately, efficiently, and transparently
Large Language Models increasingly rely on retrieval mechanisms to access structured knowledge at runtime.
This best practice outlines how ontology-aware search indexes support reliable semantic annotation and scalable knowledge extraction in AI systems.
Key highlights:
- Ontology-aware Retrieval: Treats ontology concepts as rich knowledge objects rather than simple labels, improving semantic understanding.
- Hybrid Search Strategies: Combines semantic vector search with keyword-based retrieval to handle large and complex ontologies effectively.
- Recall-first Index Design: Prioritises inclusive candidate retrieval to support accurate downstream reasoning by LLMs.
- LLM-ready Index Outputs: Structures retrieved information to fit LLM context constraints while preserving interpretability.
- Reproducibility and Provenance: Emphasises versioning, documentation, and transparent indexing pipelines for trustworthy AI systems
