@InProceedings{wang-EtAl:2018:BioNLP181,
  author    = {Wang, Lucy  and  Bhagavatula, Chandra  and  Neumann, Mark  and  Lo, Kyle  and  Wilhelm, Chris  and  Ammar, Waleed},
  title     = {Ontology alignment in the biomedical domain using entity definitions and context},
  booktitle = {Proceedings of the BioNLP 2018 workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {47--55},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-2306}
}