  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.
