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.

本文提出了一种丰富本体的实体信息的方法，并使用此方法进行本体对齐，并开发了一种神经网络结构，能够编码外部数据，结果显示，外部数据的添加导致本体对齐在 Ontology Alignment Evaluation Initiative 的 largebio SNOMED-NCI 子任务上的 F1 得分为 0.69，与当前技术下的实体级匹配相当。