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Ontology-aided Word2vec based Synonym Identification for Ontology Alignment

Synonym identification is the key factor for ontology alignment. There are several researches which proposed synonym identification methods. However, most of the researches focus on the words in general contexts, which occurs the problem in finding synonym relations in certain domains. To address this problem, we suggest ontology-aided word2vec based synonym identification method. In this paper, we find domain-specific documents based on ontology for training word2vec model. To do so, we use Kernel Density Estimation (KDE) to estimate distributions of words and we Kullback-Leibler (KL) divergence to compare the distributions. Through this, we can find the synonym relations considering domain-specific context which is hard to be identified with existing methods.