{"type":"ScholarlyArticle","title":"Iterative Entity Alignment via Joint Knowledge Embeddings","authors":[{"name":"Hao Zhu","slug":"hao-zhu","given":"Hao","family":"Zhu","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,"},{"name":"Ruobing Xie","slug":"ruobing-xie","given":"Ruobing","family":"Xie","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,"},{"name":"Zhiyuan Liu","slug":"zhiyuan-liu","given":"Zhiyuan","family":"Liu","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , Xuzhou 221009 China; National Lab for Information Science and Technology , Tsinghua University , Beijing , China"},{"name":"Maosong Sun","slug":"maosong-sun","given":"Maosong","family":"Sun","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , Xuzhou 221009 China"}],"abstract":"Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly manual feature construction to complete alignment. In this paper, we present a novel approach for entity alignment via joint knowledge embeddings. Our method jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities. During this process, we can align entities according to their semantic distance in this joint semantic space. More specifically, we present an iterative and parameter sharing method to improve alignment performance. Experiment results on real-world datasets show that, as compared to baselines, our method achieves significant improvements on entity alignment, and can further improve knowledge graph completion performance on various KGs with the favor of joint knowledge embeddings.","doi":"10.24963/ijcai.2017/595","url":"https://ijcai.org/proceedings/2017/595","pdf":"https://ijcai.org/proceedings/2017/0595.pdf","markdown":"https://ijcai.org/proceedings/2017/595.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/595","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":595,"pages":{"first":4258,"last":4264},"keywords":["Natural Language Processing: Natural Language Semantics","Natural Language Processing: Natural Language Processing","Natural Language Processing: Information Extraction"],"videos":[],"references":[{"position":1,"raw":"[Ahn et al., 2016] Sungjin Ahn, Heeyoul Choi, Tanel Pärnamaa, and Yoshua Bengio. 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