Skip to main content
SpringerLink
Log in

Translation Embeddings for Knowledge Graph Completion in Consumer Banking Sector

  • Conference paper
  • First Online:
Artificial Intelligence. IJCAI 2019 International Workshops (IJCAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12158))

Included in the following conference series:

  • 447 Accesses

Abstract

Knowledge graphs have shown many successful applications such as recommendation. One essential challenge of knowledge graph is link prediction, which is to estimate the probability of a link between two nodes based on the existing graph. Most state-of-art models to solve this problem are built using embeddings. Experiments of previous works have been conducted on WN18 and FB15K datasets to compare the performance. However, both datasets have significantly different properties compared with financial data on which there is no benchmarking of link prediction models. In this paper, we run extensive experiments of recent models on real financial data, compare their performance deeply, and show the usage of a completed knowledge graph in consumer banking sector.

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

Access this chapter

Log in via an institution
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

Notes

  1. 1.

    https://en.wikipedia.org/wiki/List_of_largest_banks_in_Southeast_Asia.

  2. 2.

    The implementation uses public code at https://github.com/mana-ysh/knowledge-graph-embeddings with reduced running time.

References

  1. Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)

    Google Scholar 

  2. Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (2011)

    Google Scholar 

  3. Krompaß, D., Baier, S., Tresp, V.: Type-constrained representation learning in knowledge graphs. In: International Semantic Web Conference, pp. 640–655 (2015)

    Google Scholar 

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

    Google Scholar 

  5. Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  6. Dong, X., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601–610 (2014)

    Google Scholar 

  7. Ebisu, T., Ichise, R.: TorusE: knowledge graph embedding on a lie group. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  8. Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)

    Article  Google Scholar 

  9. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015)

    Google Scholar 

  10. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, NIPS, pp. 3111–3119 (2013)

    Google Scholar 

  11. Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  12. Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning, ICML, pp. 809–816 (2011)

    Google Scholar 

  13. Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 1955–1961 (2016)

    Google Scholar 

  14. Qian, W., Fu, C., Zhu, Y., Cai, D., He, X.: Translating embeddings for knowledge graph completion with relation attention mechanism. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, pp. 4286–4292 (2018)

    Google Scholar 

  15. Shi, B., Weninger, T.: Proje: embedding projection for knowledge graph completion. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 1236–1242 (2017)

    Google Scholar 

  16. Socher, R., Chen, D., Manning, C.D., Andrew, Y.N.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, NIPS, pp. 926–934 (2013)

    Google Scholar 

  17. Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, pp. 57–66 (2015)

    Google Scholar 

  18. Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of the 33nd International Conference on Machine Learning, ICML, pp. 2071–2080 (2016)

    Google Scholar 

  19. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)

    Google Scholar 

  20. Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. CoRR, abs/1412.6575 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DBS Bank, Singapore, Singapore

    Dongxu Shao & Rajanikanth Annam

Authors
  1. Dongxu Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajanikanth Annam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongxu Shao .

Editor information

Editors and Affiliations

  1. Sorbonne University – Sciences, Paris, France

    Amal El Fallah Seghrouchni

  2. Bar-Ilan University, Ramat Gan, Israel

    David Sarne

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shao, D., Annam, R. (2020). Translation Embeddings for Knowledge Graph Completion in Consumer Banking Sector. In: El Fallah Seghrouchni, A., Sarne, D. (eds) Artificial Intelligence. IJCAI 2019 International Workshops. IJCAI 2019. Lecture Notes in Computer Science(), vol 12158. Springer, Cham. https://doi.org/10.1007/978-3-030-56150-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-56150-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-56149-9

  • Online ISBN: 978-3-030-56150-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation