[2603.01690] QIME: Constructing Interpretable Medical Text Embeddings via Ontology-Grounded Questions

Computer Science > Computation and Language arXiv:2603.01690 (cs) [Submitted on 2 Mar 2026] Title:QIME: Constructing Interpretable Medical Text Embeddings via Ontology-Grounded Questions Authors:Yixuan Tang, Zhenghong Lin, Yandong Sun, Anthony K.H. Tung View a PDF of the paper titled QIME: Constructing Interpretable Medical Text Embeddings via Ontology-Grounded Questions, by Yixuan Tang and 3 other authors View PDF HTML (experimental) Abstract:While dense biomedical embeddings achieve strong performance, their black-box nature limits their utility in clinical decision-making. Recent question-based interpretable embeddings represent text as binary answers to natural-language questions, but these approaches often rely on heuristic or surface-level contrastive signals and overlook specialized domain knowledge. We propose QIME, an ontology-grounded framework for constructing interpretable medical text embeddings in which each dimension corresponds to a clinically meaningful yes/no question. By conditioning on cluster-specific medical concept signatures, QIME generates semantically atomic questions that capture fine-grained distinctions in biomedical text. Furthermore, QIME supports a training-free embedding construction strategy that eliminates per-question classifier training while further improving performance. Experiments across biomedical semantic similarity, clustering, and retrieval benchmarks show that QIME consistently outperforms prior interpretable embedding methods ...