{"type":"ScholarlyArticle","title":"Multi-view Knowledge Graph Embedding for Entity Alignment","authors":[{"name":"Qingheng Zhang","slug":"qingheng-zhang","given":"Qingheng","family":"Zhang","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"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":"Muhao Chen","slug":"muhao-chen","given":"Muhao","family":"Chen","affiliation":"Department of Computer Science , University of California , Los Angeles , USA"},{"name":"Lingbing Guo","slug":"lingbing-guo","given":"Lingbing","family":"Guo","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China; The Richelieu Wing"},{"name":"Yuzhong Qu","slug":"yuzhong-qu","given":"Yuzhong","family":"Qu","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"}],"abstract":"We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.","doi":"10.24963/ijcai.2019/754","url":"https://ijcai.org/proceedings/2019/754","pdf":"https://ijcai.org/proceedings/2019/0754.pdf","markdown":"https://ijcai.org/proceedings/2019/754.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/754","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":754,"pages":{"first":5429,"last":5435},"keywords":["Natural Language Processing: Knowledge Extraction","Natural Language Processing: Embeddings"],"videos":[],"references":[{"position":1,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Al- berto García-Durán, Jason Weston, and Oksana Yakhnenko. 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