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IJMLC 2020 Vol.10(5): 605-611 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.5.980

A Joint Embedding Method of Relations and Attributes for Entity Alignment

Haihong E, Rui Cheng, Meina Song, Peican Zhu, and Zhen Wang

Abstract—Entity alignment is to link the entities that point to same objects in the real world among different knowledge graphs (KGs). Existing kn10owledge-embedding-based entity alignment methods mostly regard KG as relation triples, while ignoring attributes and attribute values in KG. However, attribute information provides a valid information supplement for relation triple, alleviates relation triple's relation universality problem and information incompleteness problem, and improves accuracy of entity alignment task. In this paper, we make the first attempt towards combing relation and attribute triples for entity alignment. We divide a KG into relation triples and attribute triples, use parameter sharing (PS) joint method and translation-based knowledge embedding methods to embed them jointly. In addition, we design two strategies: direct accumulation and weight assignment strategy, to explore the effect of relation and attribute triple's embedding on experiment performance. The experimental results show that our method has significantly improved Hits@1, Hits@10 and Mean Rank metrics compared to baseline, and is the state of the arts on entity alignment task. The source code for this paper is available from https://github.com/ChengRui536/RAKRL.

Index Terms—Data fusion, data resolution, entity alignment, knowledge graph.

Haihong E, Rui Cheng, and Meina Song are with the School of Computer Science, Beijing University of Posts and Telecommunications, China (e-mail: ehaihong@ bupt.edu.cn, chengrui536@bupt.edu.cn, mnsong@gmail.com). Peican Zhu is with the School of Computer Science and Engineering, Northwestern Polytechnical University, China (e-mail: ericcan@nwpu.edu.cn).
Zhen Wang is with the School of Mechanical Engineering, Northwestern Polytechnical University, China (e-mail: w-zhen@nwpu.edu.cn).


Cite: Haihong E, Rui Cheng, Meina Song, Peican Zhu, and Zhen Wang, "A Joint Embedding Method of Relations and Attributes for Entity Alignment," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 605-611, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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