{"type":"ScholarlyArticle","title":"Bootstrapping Entity Alignment with Knowledge Graph Embedding","authors":[{"name":"Zequn Sun","slug":"zequn-sun","given":"Zequn","family":"Sun","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Wei Hu","slug":"wei-hu","given":"Wei","family":"Hu","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Qingheng Zhang","slug":"qingheng-zhang","given":"Qingheng","family":"Zhang","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Yuzhong Qu","slug":"yuzhong-qu","given":"Yuzhong","family":"Qu","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"}],"abstract":"Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.","doi":"10.24963/ijcai.2018/611","url":"https://ijcai.org/proceedings/2018/611","pdf":"https://ijcai.org/proceedings/2018/0611.pdf","markdown":"https://ijcai.org/proceedings/2018/611.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/611","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":611,"pages":{"first":4396,"last":4402},"keywords":["Natural Language Processing: Embeddings","Multidisciplinary Topics and Applications: AI and the Web"],"videos":[],"references":[{"position":1,"raw":"[Abney, 2004] Steven P. 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