Skip to main content

TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs

  • Conference paper
  • First Online:
The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11778))

Included in the following conference series:

Abstract

Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping functions to operate on entity embeddings. However, we find that such relation-level modeling cannot capture the diverse relational structures of KGs well. In this paper, we propose a novel edge-centric embedding model TransEdge, which contextualizes relation representations in terms of specific head-tail entity pairs. We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings. TransEdge achieves promising performance on different prediction tasks. Our experiments on benchmark datasets indicate that it obtains the state-of-the-art results on embedding-based entity alignment. We also show that TransEdge is complementary with conventional entity alignment methods. Moreover, it shows very competitive performance on link prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    In the following, \((\mathtt {head}, \mathtt {relation}, \mathtt {tail})\) is abbreviated as (hrt).

  2. 2.

    http://dbpedia.org/resource/New_Zealand.

  3. 3.

    https://github.com/nju-websoft/TransEdge.

  4. 4.

    http://krrwebtools.cs.ox.ac.uk/logmap/.

References

  1. Akrami, F., Guo, L., Hu, W., Li, C.: Re-evaluating embedding-based knowledge graph completion methods. In: CIKM, pp. 1779–1782 (2018)

    Google Scholar 

  2. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)

    Google Scholar 

  3. Chen, M., Tian, Y., Chang, K., Skiena, S., Zaniolo, C.: Co-training embeddings of knowledge graphs and entity descriptions for cross-lingual entity alignment. In: IJCAI, pp. 3998–4004 (2018)

    Google Scholar 

  4. Chen, M., Tian, Y., Yang, M., Zaniolo, C.: Multilingual knowledge graph embeddings for cross-lingual knowledge alignment. In: IJCAI, pp. 1511–1517 (2017)

    Google Scholar 

  5. Cochez, M., Ristoski, P., Ponzetto, S.P., Paulheim, H.: Global RDF vector space embeddings. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 190–207. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_12

    Chapter  Google Scholar 

  6. Conneau, A., Lample, G., Ranzato, M., Denoyer, L., Jégou, H.: Word translation without parallel data. In: ICLR (2018)

    Google Scholar 

  7. Dasgupta, S.S., Ray, S.N., Talukdar, P.: HyTE: hyperplane-based temporally aware knowledge graph embedding. In: EMNLP, pp. 2001–2011 (2018)

    Google Scholar 

  8. Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: AAAI, pp. 1811–1818 (2018)

    Google Scholar 

  9. Gentile, A.L., Ristoski, P., Eckel, S., Ritze, D., Paulheim, H.: Entity matching on web tables: a table embeddings approach for blocking. In: EDBT, pp. 510–513 (2017)

    Google Scholar 

  10. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. AISTATS 9, 249–256 (2010)

    Google Scholar 

  11. Han, X., et al.: OpenKE: an open toolkit for knowledge embedding. In: EMNLP (Demonstration), pp. 139–144 (2018)

    Google Scholar 

  12. Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: ACL, pp. 687–696 (2015)

    Google Scholar 

  13. Jiménez-Ruiz, E., Grau, B.C., Zhou, Y., Horrocks, I.: Large-scale interactive ontology matching: algorithms and implementation. In: ECAI, pp. 444–449 (2012)

    Google Scholar 

  14. Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: NeurIPS, pp. 4289–4300 (2018)

    Google Scholar 

  15. Krompaß, D., Baier, S., Tresp, V.: Type-constrained representation learning in knowledge graphs. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 640–655. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_37

    Chapter  Google Scholar 

  16. Lin, Y., Liu, Z., Luan, H.B., Sun, M., Rao, S., Liu, S.: Modeling relation paths for representation learning of knowledge bases. In: ACL, pp. 705–714 (2015)

    Google Scholar 

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

    Google Scholar 

  18. Liu, H., Wu, Y., Yang, Y.: Analogical inference for multi-relational embeddings. In: ICML, pp. 2168–2178 (2017)

    Google Scholar 

  19. Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.Q.: A novel embedding model for knowledge base completion based on convolutional neural network. In: NAACL-HLT, pp. 327–333 (2018)

    Google Scholar 

  20. Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. In: AAAI, pp. 1955–1961 (2016)

    Google Scholar 

  21. Oh, B., Seo, S., Lee, K.: Knowledge graph completion by context-aware convolutional learning with multi-hop neighborhoods. In: CIKM, pp. 257–266 (2018)

    Google Scholar 

  22. Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 498–514. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46523-4_30

    Chapter  Google Scholar 

  23. Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38

    Chapter  Google Scholar 

  24. Shi, B., Weninger, T.: ProjE: embedding projection for knowledge graph completion. In: AAAI, pp. 1236–1242 (2017)

    Google Scholar 

  25. 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). https://doi.org/10.1007/978-3-319-68288-4_37

    Chapter  Google Scholar 

  26. Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: IJCAI, pp. 4396–4402 (2018)

    Google Scholar 

  27. Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: RotatE: knowledge graph embedding by relational rotation in complex space. In: ICLR (2019)

    Google Scholar 

  28. Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. In: EMNLP, pp. 1499–1509 (2015)

    Google Scholar 

  29. Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: ICML, pp. 2071–2080 (2016)

    Google Scholar 

  30. Trsedya, B.D., Qi, J., Zhang, R.: Entity alignment between knowledge graphs using attribute embeddings. In: AAAI (2019)

    Google Scholar 

  31. Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  33. Wang, Z., Lv, Q., Lan, X., Zhang, Y.: Cross-lingual knowledge graph alignment via graph convolutional networks. In: EMNLP, pp. 349–357 (2018)

    Google Scholar 

  34. Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: ICLR (2015)

    Google Scholar 

  35. Zhang, W., Paudel, B., Zhang, W., Bernstein, A., Chen, H.: Interaction embeddings for prediction and explanation in knowledge graphs. In: WSDM, pp. 96–104 (2019)

    Google Scholar 

  36. Zhu, H., Xie, R., Liu, Z., Sun, M.: Iterative entity alignment via joint knowledge embeddings. In: IJCAI, pp. 4258–4264 (2017)

    Google Scholar 

Download references

Acknowledgments

This work is funded by the National Natural Science Foundation of China (No. 61872172), and the Key R&D Program of Jiangsu Science and Technology Department (No. BE2018131).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Hu .

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

Sun, Z., Huang, J., Hu, W., Chen, M., Guo, L., Qu, Y. (2019). TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30793-6_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30792-9

  • Online ISBN: 978-3-030-30793-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics