文章基本信息

标题：Finding Similar Entities Across Knowledge Graphs
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作者：Sareh Aghaei ; Anna Fensel
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：3
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Finding similar entities among knowledge graphs is an essential research problem for knowledge integration and knowledge graph connection. This paper aims at finding semantically similar entities between two knowledge graphs. This can help end users and search agents more effectively and easily access pertinent information across knowledge graphs. Given a query entity in one knowledge graph, the proposed approach tries to find the most similar entity in another knowledge graph. The main idea is to leverage graph embedding, clustering, regression and sentence embedding. In this approach, RDF2Vec has been employed to generate vector representations of all entities of the second knowledge graph and then the vectors have been clustered based on cosine similarity using K medoids algorithm. Then, an artificial neural network with multilayer perception topology has been used as a regression model to predict the corresponding vector in the second knowledge graph for a given vector from the first knowledge graph. After determining the cluster of the predicated vector, the entities of the detected cluster are ranked through sentence-BERT method and finally the entity with the highest rank is chosen as the most similar one. To evaluate the proposed approach, experiments have been conducted on real-world knowledge graphs. The experimental results demonstrate the effectiveness of the proposed approach.
关键词：Knowledge Graph;Similar Entity;Graph Embedding;Clustering;Regression;Sentence Embedding