Skip to main content

Unsupervised Entity Alignment Using Attribute Triples and Relation Triples

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11446)


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.


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

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. 1.

  2. 2.

  3. 3.


  1. Bell, G.B., Sethi, A.: Matching records in a national medical patient index. Commun. ACM 44(9), 83–88 (2001)

    CrossRef  Google Scholar 

  2. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)

    Google Scholar 

  3. Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Saporta, G., Lechevallier, Y. (eds.) COMPSTAT 2010, pp. 177–186. Springer, Heidelberg (2010).

    CrossRef  Google Scholar 

  4. Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Mach. Learn. 15(2), 201–221 (1994)

    Google Scholar 

  5. Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: a survey. IEEE Trans. Knowl. Data Eng. 19(1), 1–16 (2007)

    CrossRef  Google Scholar 

  6. Fan, J., Lu, M., Ooi, B.C., Tan, W.C., Zhang, M.: A hybrid machine-crowdsourcing system for matching web tables. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 976–987. IEEE (2014)

    Google Scholar 

  7. Fu, B., Brennan, R., O’Sullivan, D.: Cross-lingual ontology mapping – an investigation of the impact of machine translation. In: Gómez-Pérez, A., Yu, Y., Ding, Y. (eds.) ASWC 2009. LNCS, vol. 5926, pp. 1–15. Springer, Heidelberg (2009).

    CrossRef  Google Scholar 

  8. Heeringa, W.J.: Measuring dialect pronunciation differences using Levenshtein distance. Ph.D. thesis. Citeseer (2004)

    Google Scholar 

  9. Hirschberg, D.S.: A linear space algorithm for computing maximal common subsequences. Commun. ACM 18(6), 341–343 (1975)

    CrossRef  MathSciNet  Google Scholar 

  10. Larson, J.A., Navathe, S.B., Elmasri, R.: A theory of attributed equivalence in databases with application to schema integration. IEEE Trans. Softw. Eng. 15(4), 449–463 (1989)

    CrossRef  Google Scholar 

  11. Lenzerini, M.: Data integration: a theoretical perspective. In: Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 233–246. ACM (2002)

    Google Scholar 

  12. Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379 (2015)

  13. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, vol. 15, pp. 2181–2187 (2015)

    Google Scholar 

  14. Palopoli, L., Saccá, D., Terracina, G., Ursino, D.: A unified graph-based framework for deriving nominal interscheme properties, type conflicts and object cluster similarities. In: Proceedings of 1999 IFCIS International Conference on Cooperative Information Systems, CoopIS 1999, pp. 34–45. IEEE (1999)

    Google Scholar 

  15. Perkowitz, M., Doorenbos, R.B., Etzioni, O., Weld, D.S.: Learning to understand information on the internet: an example-based approach. J. Intell. Inf. Syst. 8(2), 133–153 (1997)

    CrossRef  Google Scholar 

  16. Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4), 334–350 (2001)

    CrossRef  Google Scholar 

  17. Sun, Z., Hu, W., Li, C.: Cross-lingual entity alignment via joint attribute-preserving embedding. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 628–644. Springer, Cham (2017).

    CrossRef  Google Scholar 

  18. Verykios, V.S., Elmagarmid, A.K., Houstis, E.N.: Automating the approximate record-matching process. Inf. Sci. 126(1–4), 83–98 (2000)

    CrossRef  Google Scholar 

  19. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, vol. 14, pp. 1112–1119 (2014)

    Google Scholar 

  20. Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it? a new look at signal fidelity measures. IEEE Sig. Process. Mag. 26(1), 98–117 (2009)

    CrossRef  Google Scholar 

  21. Yang, J., Fan, J., Wei, Z., Li, G., Liu, T., Du, X.: Cost-effective data annotation using game-based crowdsourcing. Proc. VLDB Endow. 12(1), 57–70 (2018)

    CrossRef  Google Scholar 

Download references


This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61572336, 61572335, 61772356), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010), and the Open Program of Neusoft Corporation (No. SKLSAOP1801).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Zhixu Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

He, F. et al. (2019). Unsupervised Entity Alignment Using Attribute Triples and Relation Triples. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18575-6

  • Online ISBN: 978-3-030-18576-3

  • eBook Packages: Computer ScienceComputer Science (R0)