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
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, email@example.com, firstname.lastname@example.org). Peican Zhu is with the School of Computer Science and Engineering, Northwestern Polytechnical University, China (e-mail: email@example.com).
Zhen Wang is with the School of Mechanical Engineering, Northwestern Polytechnical University, China (e-mail: firstname.lastname@example.org).
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).