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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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A Comprehensive Evaluation of Biomedical Entity Linking Models.

David Kartchner^1,2, Jennifer Deng², Shubham Lohiya²

¹Enveda Biosciences.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing

|May 17, 2024

Summary

This summary is machine-generated.

This study evaluates nine biomedical entity linking (BioEL) models, finding current methods struggle with gene/protein linking and context integration. A unified framework and models are released to aid future research.

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Area of Science:

Biomedical Informatics
Natural Language Processing
Computational Biology

Background:

Biomedical entity linking (BioEL) is crucial for connecting text mentions to structured knowledge bases like UMLS and MeSH.
Existing BioEL models vary significantly, necessitating a standardized evaluation approach.

Purpose of the Study:

To comprehensively evaluate nine state-of-the-art BioEL models using a unified framework.
To identify strengths and weaknesses of current BioEL approaches regarding accuracy, speed, usability, generalization, and adaptability.
To provide a reproducible evaluation framework and released models for future research.

Main Methods:

Developed and applied a unified framework to assess nine BioEL models.
Compared models based on accuracy, speed, ease of use, generalization, and adaptability to new ontologies/datasets.
Quantified the impact of preprocessing steps, including abbreviation detection.

Main Results:

Current BioEL models exhibit notable performance gaps, particularly in linking genes and proteins.
Models often struggle to effectively incorporate contextual information for accurate entity disambiguation.
Performance variations were observed across different evaluation criteria and datasets.

Conclusions:

A significant need exists for improved BioEL methods, especially for gene and protein entities.
Enhanced context utilization is critical for advancing BioEL accuracy.
The released unified framework and models will serve as a valuable resource for the BioEL research community.