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BELB: a biomedical entity linking benchmark.

Samuele Garda¹, Leon Weber-Genzel², Robert Martin¹

¹Computer Science Department, Humboldt-Universität zu Berlin, Berlin 10099, Germany.

Bioinformatics (Oxford, England)

|November 17, 2023

Summary

This summary is machine-generated.

A new benchmark, BELB, standardizes biomedical entity linking (BEL) experiments across multiple corpora and knowledge bases. Current neural models struggle with diverse entity types, indicating a need for more robust, entity-agnostic approaches in life sciences text mining.

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Area of Science:

Life Sciences
Biomedical Informatics
Natural Language Processing

Background:

Biomedical entity linking (BEL) is crucial for extracting information from life sciences literature.
Existing benchmarks lack standardization, hindering reproducible comparisons of BEL systems.
Neural BEL systems are under-evaluated on specialized knowledge bases and entity types.

Approach:

Developed BELB, a unified benchmark for biomedical entity linking.
Included 11 corpora linked to 7 knowledge bases, covering 6 entity types (gene, disease, chemical, species, cell line, variant).
Evaluated 6 rule-based and 3 neural BEL systems using the BELB benchmark.

Key Points:

BELB offers a standardized testbed, reducing preprocessing for multi-corpus BEL system evaluation.
Neural approaches show inconsistent performance across different entity types.
Rule-based systems demonstrate competitive performance, particularly for specific entity types.

Conclusions:

BELB facilitates reproducible research and direct comparison of BEL methods.
Further research is needed to develop entity-agnostic neural models for BEL.
The benchmark supports evaluation on specialized biomedical corpora and knowledge bases.