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BELHD: improving biomedical entity linking with homonym disambiguation.

Samuele Garda¹, Ulf Leser¹

¹Computer Science, Humboldt-Universität zu Berlin, Berlin 12489, Germany.

Bioinformatics (Oxford, England)

|July 27, 2024

Summary

Biomedical entity linking (BEL) methods struggle with homonyms. BELHD introduces KB preprocessing and candidate sharing to improve disambiguation, achieving state-of-the-art results in multiple corpora.

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Area of Science:

Biomedical informatics
Computational biology
Natural language processing

Background:

Biomedical entity linking (BEL) grounds mentions to knowledge bases (KBs).
Neural name-based BEL methods excel but fail with homonyms (multiple entities with the same name).
Homonyms significantly impact BEL performance in large KBs like UMLS and NCBI Gene.

Purpose of the Study:

To address the challenge of homonyms in neural name-based BEL.
To introduce a novel method, BELHD (Biomedical Entity Linking with Homonym Disambiguation), for improved entity linking accuracy.

Main Methods:

BELHD extends the BioSyn model with two key strategies.
KB preprocessing: Expanding homonyms with unique disambiguating strings.
Candidate sharing: Incorporating similar mentions within a document to enhance training signals.

Main Results:

BELHD achieved state-of-the-art performance across 10 corpora and 5 entity types.
Significant improvement in recall@1, averaging 4.55pp across tested datasets.
Demonstrated superior results in 6 out of 10 corpora compared to existing neural BEL approaches.

Conclusions:

BELHD effectively resolves homonyms in biomedical entity linking.
The KB preprocessing component is model-agnostic and can enhance other neural BEL methods.
BELHD represents a significant advancement for accurate biomedical entity recognition and linking.