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.
@article{arxiv.1806.07976,
title = {Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context},
author = {Lucy Lu Wang and Chandra Bhagavatula and Mark Neumann and Kyle Lo and Chris Wilhelm and Waleed Ammar},
journal= {arXiv preprint arXiv:1806.07976},
year = {2018}
}