MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking

TL;DR

MedPath introduces a comprehensive biomedical entity linking dataset with multi-domain, hierarchical, and ontology-enriched annotations to advance explainable and interoperable clinical NLP systems.

Contribution

It provides the first large-scale, multi-domain biomedical EL dataset with hierarchical ontological paths and extensive vocabulary mappings, addressing data fragmentation and explainability.

Findings

Enables training of semantic-rich EL models

Facilitates evaluation with ontological context

Supports development of explainable NLP systems

Abstract

Progress in biomedical Named Entity Recognition (NER) and Entity Linking (EL) is currently hindered by a fragmented data landscape, a lack of resources for building explainable models, and the limitations of semantically-blind evaluation metrics. To address these challenges, we present MedPath, a large-scale and multi-domain biomedical EL dataset that builds upon nine existing expert-annotated EL datasets. In MedPath, all entities are 1) normalized using the latest version of the Unified Medical Language System (UMLS), 2) augmented with mappings to 62 other biomedical vocabularies and, crucially, 3) enriched with full ontological paths -- i.e., from general to specific -- in up to 11 biomedical vocabularies. MedPath directly enables new research frontiers in biomedical NLP, facilitating training and evaluation of semantic-rich and interpretable EL systems, and the development of the next generation of interoperable and explainable clinical NLP models.