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.
Abstract (translated)
本体对齐是识别两个给定本体的语义等价实体的任务。不同的本体具有同一实体的不同表示，导致在合并本体时需要去重复实体。我们提出了一种方法来丰富具有外部定义和上下文信息的本体中的实体，并使用这些附加信息进行本体对齐。我们开发了一种能够在可用时编码附加信息的神经结构，并且显示在本体对齐评估计划（OAEI）大规模SNOMED-NCI子任务中添加外部数据得到0.69的F1分数， SOTA系统中的高级匹配器。
URL
https://arxiv.org/abs/1806.07976
