用语言模型嵌入在双曲空间中实现罕见词查询的医学概念检索
Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT
- 将医学概念映射到双曲空间，利用语言模型嵌入进行层级推理
- 在罕见词查询下，对最具体匹配项的检索准确率超越基线方法
- 适用于医疗知识图谱，尤其适合处理未登录词的精准查询
SNOMED CT 是一个大规模的生物医学本体，采用层级结构表示术语概念。其知识检索对实际应用至关重要，但常受语言模糊、同义词、多义性等问题困扰，尤其当查询词为未登录词（OOV）时，缺乏与本体中任何概念的直接匹配，问题更加严重。本文聚焦于使用 OOV 查询从 SNOMED CT 中进行层级概念检索，提出一种基于语言模型本体嵌入的方法，将概念嵌入双曲空间，以支持文本查询与任意概念间的高效蕴含推理。我们构建了三个数据集，其中对 OOV 查询标注了与 SNOMED CT 概念的对应关系，测试最具体蕴含者及其相关祖先的检索性能。实验表明，该方法优于 SBERT、SapBERT 及两种词汇匹配方法。尽管以 SNOMED CT 为评估对象，该方法具备可扩展性，可推广至其他本体。代码与数据集已开源。
原文摘要 · Abstract (English)
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.
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