HyEm: Query-Adaptive Hyperbolic Retrieval for Biomedical Ontologies via Euclidean Vector Indexing

TL;DR

HyEm introduces a hybrid retrieval method combining hyperbolic and Euclidean embeddings to improve hierarchy-aware biomedical ontology retrieval while maintaining compatibility with existing vector databases.

Contribution

HyEm provides a novel lightweight retrieval layer that integrates hyperbolic embeddings into Euclidean ANN systems with query-adaptive mixing, enhancing hierarchy-aware retrieval.

Findings

HyEm preserves 94-98% of Euclidean baseline performance on entity-centric queries.

HyEm significantly improves hierarchy navigation and mixed-intent query handling.

HyEm maintains indexability at moderate oversampling levels.

Abstract

Retrieval-augmented generation (RAG) for biomedical knowledge faces a hierarchy-aware ontology grounding challenge: resources like HPO, DO, and MeSH use deep ``is-a" taxonomies, yet production stacks rely on Euclidean embeddings and ANN indexes. While hyperbolic embeddings suit hierarchical representation, they face two barriers: (i) lack of native vector database support, and (ii) risk of underperforming on entity-centric queries where hierarchy is irrelevant. We present HyEm, a lightweight retrieval layer integrating hyperbolic ontology embeddings into existing Euclidean ANN infrastructure. HyEm learns radius-controlled hyperbolic embeddings, stores origin log-mapped vectors in standard Euclidean databases for candidate retrieval, then applies exact hyperbolic reranking. A query-adaptive gate outputs continuous mixing weights, combining Euclidean semantic similarity with hyperbolic hierarchy distance at reranking time. Our bi-Lipschitz analysis under radius constraints provides practical guidance for ANN oversampling and dimensionality.Experiments on biomedical ontology subsets demonstrate HyEm preserves 94-98% of Euclidean baseline performance on entity-centric queries while substantially improving hierarchy-navigation and mixed-intent queries, maintaining indexability at moderate oversampling.