TitleERSOM: A structural ontology matching approach using automatically learned entity representation
AuthorsXiang, Chuncheng
Jiang, Tingsong
Chang, Baobao
Sui, Zhifang
AffiliationKey Laboratory of Computational Linguistics, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Collaborative Innovation Center for Language Ability, Xuzhou, China
Issue Date2015
PublisherConference on Empirical Methods in Natural Language Processing, EMNLP 2015
CitationConference on Empirical Methods in Natural Language Processing, EMNLP 2015.Lisbon, Portugal,2015/1/1.
AbstractAs a key representation model of knowledge, ontology has been widely used in a lot of NLP related tasks, such as semantic parsing, information extraction and text mining etc. In this paper, we study the task of ontology matching, which concentrates on finding semantically related entities between different ontologies that describe the same domain, to solve the semantic heterogeneity problem. Previous works exploit different kinds of descriptions of an entity in ontology directly and separately to find the correspondences without considering the higher level correlations between the descriptions. Besides, the structural information of ontology haven't been utilized adequately for ontology matching. We propose in this paper an ontology matching approach, named ERSOM, which mainly includes an unsupervised representation learning method based on the deep neural networks to learn the general representation of the entities and an iterative similarity propagation method that takes advantage of more abundant structure information of the ontology to discover more mappings. The experimental results on the datasets from Ontology Alignment Evaluation Initiative (OAEI1) show that ER-SOM achieves a competitive performance compared to the state-of-the-art ontology matching systems. ? 2015 Association for Computational Linguistics.
Appears in Collections:信息科学技术学院

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