BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation
Samuele Garda Ulf Leser
Biomedical entity linking (BEL) is the task of grounding entity mentions to a
knowledge base (KB). A popular approach to the task are name-based methods,
i.e. those identifying the most appropriate name in the KB for a given mention,
either via dense retrieval or autoregressive modeling. However, as these
methods directly return KB names, they cannot cope with homonyms, i.e.
different KB entities sharing the exact same name. This significantly affects
their performance, especially for KBs where homonyms account for a large amount
of entity mentions (e.g. UMLS and NCBI Gene). We therefore present BELHD
(Biomedical Entity Linking with Homonym Disambiguation), a new name-based
method that copes with this challenge. Specifically, BELHD builds upon the
BioSyn (Sung et al.,2020) model introducing two crucial extensions. First, it
performs a preprocessing of the KB in which it expands homonyms with an
automatically chosen disambiguating string, thus enforcing unique linking
decisions. Second, we introduce candidate sharing, a novel strategy to select
candidates for contrastive learning that enhances the overall training signal.
Experiments with 10 corpora and five entity types show that BELHD improves upon
state-of-the-art approaches, achieving the best results in 6 out 10 corpora
with an average improvement of 4.55pp recall@1. Furthermore, the KB
preprocessing is orthogonal to the core prediction model and thus can also
improve other methods, which we exemplify for GenBioEL (Yuan et al, 2022), a
generative name-based BEL approach. Code is available at: link added upon
publication.