Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT

TL;DR

This paper introduces a novel method for hierarchical concept retrieval in SNOMED CT using language model-based ontology embeddings in hyperbolic space, effectively handling out-of-vocabulary queries.

Contribution

The work presents a new approach utilizing hyperbolic embeddings for OOV query retrieval in SNOMED CT, outperforming existing baselines and generalizable to other ontologies.

Findings

Our method outperforms SBERT, SapBERT, and lexical matching baselines.

Constructed datasets demonstrate the effectiveness of hyperbolic embeddings for OOV queries.

Approach is applicable to other ontologies beyond SNOMED CT.

Abstract

SNOMED CT is a biomedical ontology with a hierarchical representation, modelling terminological concepts at a large scale. Knowledge retrieval in SNOMED CT is critical for its application but often proves challenging due to linguistic ambiguity, synonymy, polysemy, and so on. This problem is exacerbated when the queries are out-of-vocabulary (OOV), i.e., lacking any equivalent matches in the ontology. In this work, we focus on the problem of hierarchical concept retrieval from SNOMED CT with OOV queries, and propose an approach driven by utilising language model-based ontology embeddings, which represent hierarchical concepts in a hyperbolic space for enabling efficient subsumption inference between a textual query and an arbitrary concept. For evaluation, we construct three datasets where OOV queries are annotated against SNOMED CT concepts, testing the retrieval of the most specific subsumers and their less relevant ancestors. We find that our method outperforms the baselines, including SBERT, SapBERT, and two lexical matching methods. While evaluated against SNOMED CT, the approach is generalisable and can be extended to other ontologies. We release all the experiment codes and datasets at https://github.com/jonathondilworth/HR-OOV-SNOMED-CT.