Ontology alignment in the biomedical domain using entity definitions and context

Lucy Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm, Waleed Ammar


Abstract
Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.
Anthology ID:
W18-2306
Volume:
Proceedings of the BioNLP 2018 workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
47–55
Language:
URL:
https://aclanthology.org/W18-2306
DOI:
10.18653/v1/W18-2306
Bibkey:
Cite (ACL):
Lucy Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm, and Waleed Ammar. 2018. Ontology alignment in the biomedical domain using entity definitions and context. In Proceedings of the BioNLP 2018 workshop, pages 47–55, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Ontology alignment in the biomedical domain using entity definitions and context (Wang et al., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2306.pdf
Code
 allenai/ontoemma
Data
Semantic Scholar