Skip to main content

XTransE: Explainable Knowledge Graph Embedding for Link Prediction with Lifestyles in e-Commerce

Part of the Communications in Computer and Information Science book series (CCIS,volume 1157)

Abstract

In e-Commerce, we are interested in deals by lifestyle which will improve the diversity of items shown to users. A lifestyle, an important motivation for consumption, is a person’s pattern of living in the world as expressed in activities, interests, and opinions. In this paper, we focus on the key task for deals by lifestyle, establishing linkage between items and lifestyles. We build an item-lifestyle knowledge graph to fully utilize the information about them and formulate it as a knowledge graph link prediction task. A lot of knowledge graph embedding methods are proposed to accomplish relational learning in academia. Although these methods got impressive results on benchmark datasets, they can’t provide insights and explanations for their prediction which limit their usage in industry. In this scenario, we concern about not only linking prediction results, but also explanations for predicted results and human-understandable rules, because explanations help us deal with uncertainty from algorithms and rules can be easily transferred to other platforms. Our proposal includes an explainable knowledge graph embedding method (XTransE), an explanation generator and a rule collector, which outperforms traditional classifier models and original embedding method during prediction, and successfully generates explanations and collects meaningful rules.

Keywords

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

Buying options

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

Learn about institutional subscriptions

References

  1. 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 

  2. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  3. Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.M.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: WWW, pp. 413–422 (2013)

    Google Scholar 

  4. Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)

    Article  Google Scholar 

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

    Google Scholar 

  6. Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 4, 580–585 (1985)

    Article  Google Scholar 

  7. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. abs/1412.6980 (2014)

    Google Scholar 

  8. Kotler, P.: Marketing Management (2012)

    Google Scholar 

  9. Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R N. 2(3), 18–22 (2002)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  12. Murphy, K.P., et al.: Naive bayes classifiers. Univ. Brit. Columbia 18, 60 (2006)

    Google Scholar 

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

    Google Scholar 

  14. Omran, P.G., Wang, K., Wang, Z.: Scalable rule learning via learning representation. In: IJCAI, pp. 2149–2155. ijcai.org (2018)

    Google Scholar 

  15. Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)

    Article  MathSciNet  Google Scholar 

  16. Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion, pp. 926–934 (2013)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  20. Zhang, W., et al.: Iteratively learning embeddings and rules for knowledge graph reasoning. In: WWW, pp. 2366–2377. ACM (2019)

    Google Scholar 

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

    Google Scholar 

Download references

Acknowledgments

This work is funded by NSFC 91846204/61473260, national key research program YS2018YFB140004, and Alibaba CangJingGe (Knowledge Engine) Research Plan.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huajun Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, W., Deng, S., Wang, H., Chen, Q., Zhang, W., Chen, H. (2020). XTransE: Explainable Knowledge Graph Embedding for Link Prediction with Lifestyles in e-Commerce. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3412-6_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3411-9

  • Online ISBN: 978-981-15-3412-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics