Unsupervised entity alignment using attribute triples and relation triples

Fuzhen He, Zhixu Li*, Yang Qiang, An Liu, Guanfeng Liu, Pengpeng Zhao, Lei Zhao, Min Zhang, Zhigang Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

29 Citations (Scopus)


Entity alignment aims to find entities referring to the same real-world object across different knowledge graphs (KGs). Most existing works utilize the relations between entities contained in the relation triples with embedding-based approaches, but require a large number of training data. Some recent attempt works on using types of their attributes in attribute triples for measuring the similarity between entities across KGs. However, due to diverse expressions of attribute names and non-standard attribute values across different KGs, the information contained in attribute triples can not be fully used. To tackle the drawbacks of the existing efforts, we novelly propose an unsupervised entity alignment approach using both attribute triples and relation triples of KGs. Initially, we propose an interactive model to use attribute triples by performing entity alignment and attribute alignment alternately, which will generate a lot of high-quality aligned entity pairs. We then use these aligned entity pairs to train a relation embedding model such that we could use relation triples to further align the remaining entities. Lastly, we utilize a bivariate regression model to learn the respective weights of similarities measuring from the two aspects for a result combination. Our empirical study performed on several real-world datasets shows that our proposed method achieves significant improvements on entity alignment compared with state-of-the-art methods.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publication24th International Conference, DASFAA 2019, Proceedings, Part I
EditorsGuoliang Li, Jun Yang, Joao Gama, Juggapong Natwichai, Yongxin Tong
Place of PublicationSwitzerland
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Number of pages16
ISBN (Electronic)9783030185763
ISBN (Print)9783030185756
Publication statusPublished - 1 Jan 2019
Event24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand
Duration: 22 Apr 201925 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11446 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
CityChiang Mai


  • Attribute triples
  • Bivariate regression model
  • Interactive model
  • Relation triples
  • Unsupervised entity alignment


Dive into the research topics of 'Unsupervised entity alignment using attribute triples and relation triples'. Together they form a unique fingerprint.

Cite this