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    <abstract>Bi-encoder architectures for biomedical entity linking learn flat embedding spaces that ignore ontology structure. HiCo-EL encodes hierarchical knowledge into bi-encoder representations through hierarchical contrastive pretraining. The core idea is within-space hierarchical multi-label contrastive learning (HMLC): contrastive losses applied independently within each encoder space, preserving cross-space compatibility for retrieval. Our formulation drops the monotonicity constraints of prior HMLC work, handles variable-depth hierarchy paths, and adds depth-weighted alignment between encoder spaces. Treating each entity as a leaf node unifies coarse type structure with fine-grained entity discrimination. On MedMentions and NCBI Disease, HiCo-EL outperforms ArboEL by +3.7% and +1.8% Recall@1 respectively. The gains reveal a key insight: breadth amplifies, depth generalizes—broad hierarchies help common entities most (+6.3% R@1), while deep hierarchies benefit unseen entities (+9.6% R@1 zero-shot).</abstract>
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%0 Conference Proceedings
%T HiCo-EL: Encoding Ontology Structure into Bi-Encoder Entity Linking
%A Lee, Albert
%A Bathala, Prasanth
%A Malhotra, Puru
%A Ye, Christophe
%A Mitchell, Cassie S.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lee-etal-2026-hico
%X Bi-encoder architectures for biomedical entity linking learn flat embedding spaces that ignore ontology structure. HiCo-EL encodes hierarchical knowledge into bi-encoder representations through hierarchical contrastive pretraining. The core idea is within-space hierarchical multi-label contrastive learning (HMLC): contrastive losses applied independently within each encoder space, preserving cross-space compatibility for retrieval. Our formulation drops the monotonicity constraints of prior HMLC work, handles variable-depth hierarchy paths, and adds depth-weighted alignment between encoder spaces. Treating each entity as a leaf node unifies coarse type structure with fine-grained entity discrimination. On MedMentions and NCBI Disease, HiCo-EL outperforms ArboEL by +3.7% and +1.8% Recall@1 respectively. The gains reveal a key insight: breadth amplifies, depth generalizes—broad hierarchies help common entities most (+6.3% R@1), while deep hierarchies benefit unseen entities (+9.6% R@1 zero-shot).
%U https://aclanthology.org/2026.findings-emnlp.103/
%P 2113-2123

Markdown (Informal)

[HiCo-EL: Encoding Ontology Structure into Bi-Encoder Entity Linking](https://aclanthology.org/2026.findings-emnlp.103/) (Lee et al., Findings 2026)

• HiCo-EL: Encoding Ontology Structure into Bi-Encoder Entity Linking (Lee et al., Findings 2026)

ACL

• Albert Lee, Prasanth Bathala, Puru Malhotra, Christophe Ye, and Cassie S. Mitchell. 2026. HiCo-EL: Encoding Ontology Structure into Bi-Encoder Entity Linking. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2113–2123, Budapest, Hungary. Association for Computational Linguistics.