Biomedical ontology normalization maps free-text expressions to standardized concepts, enabling consistent integration and analysis of biomedical data. This task remains challenging because lexical va...
arXiv:2609.08174v1 Announce Type: new Abstract: We introduce OntologyBench, a tiered biomedical retrieval benchmark comprising 471,854 training and 125,744 evaluation query-document relevance pairs a...
arXiv:2608. 04144v1 Announce Type: cross Abstract: Biomedical entity linking grounds mentions in clinical and scientific text to entities in a curated knowledge base (KB) with ontological structure, which supports downstream applications such as literature-scale information extraction and patient-record normalization.
The paper introduces Hyperbolic Clinical Ontology Embeddings (HCOE), a method that transforms frozen BioBERT embeddings into a Poincaré ball to capture medical code hierarchies. HCOE employs ontology-guided contrastive learning and coarse‑to‑fine ontology‑path aggregation, leveraging ICD, CCS, and ATC hierarchies. Experiments on MIMIC‑IV demonstrate superior performance in clinical relation prediction, hierarchy transfer, and various predictive tasks such as mortality, readmission, medication recommendation, and rare drug prediction.
arXiv:2610.18672v1 Announce Type: cross Abstract: Biomedical language models (LMs) provide strong representations for clinical tasks, but they often fail to capture the hierarchical structure among m...
arXiv:2608.31118v1 Announce Type: new Abstract: The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled eval...
arXiv:2608.03154v2 Announce Type: replace Abstract: Biomedical relation extraction (BioRE) supports knowledge graph construction and hypothesis generation. In real-world settings, most candidate enti...
arXiv:2605. 28965v2 Announce Type: replace Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration of comparative morphological data.
arXiv:2607. 01977v1 Announce Type: new Abstract: Ontology learning (OL) aims to automatically construct structured knowledge models from text, yet progress remains fragmented across methods, domains, and evaluation practices.
arXiv:2607. 18927v1 Announce Type: new Abstract: We present OntoBook, a method that converts medical ontology structure into pretraining signal for encoder language models.
The paper presents a lightweight, scalable workflow for refining ontologies, demonstrated on the CASTON casting-domain ontology. It combines a fine‑tuned ontology embedding model to generate candidate axioms and replacement options with a Large Language Model (LLM) that proposes repair suggestions. In a small expert evaluation, the default setup—using a CASTON‑fine‑tuned embedding model and Qwen3‑8B—yielded high‑confidence repairs that were all accepted by domain experts.
The paper proposes a behavior-based fusion model that combines large language models (LLMs) with ontology rankers to improve rare-disease diagnosis. By examining ranked lists, agreement, and ontology support, the model learns how much to rely on each system per case, achieving significant recall gains on Phenopacket Store and RAMEDIS benchmarks. Importantly, the fused diagnoses retain candidate-level ontology evidence for inspection.