Skip to main content
SpringerLink
Log in
Book cover

International Conference on Web Information Systems Engineering

WISE 2020: Web Information Systems Engineering – WISE 2020 pp 273–285Cite as

Knowledge-Infused Pre-trained Models for KG Completion

  • Conference paper
  • First Online:

  • 1509 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12342))

Abstract

Knowledge graphs (KG) are the basis for many artificial intelligence applications but still suffer from incompleteness. In this paper, we introduce a novel method for KG completion task by knowledge-infused pre-trained language models. We represent each triple in the KG as textual sequences and transform the KG completion task into a sentence classification task that fits the input of the language model. Our KG completion framework based on the knowledge-infused pre-trained language model which can capture both linguistic information and factual knowledge to compute the plausible of the triples. Experiments show that our method achieves better results than previous state-of-the-art on multiple benchmark datasets.

The work described in this paper is partially supported by the National Key Research and Development Program of China (No. 2017YFB0802204, 2016QY03D0603, 2016QY03D0601, 2017YFB0803301, 2019QY1406), the Key R&D Program of Guangdong Province (No. 2019B010136003), and the National Natural Science Foundation of China (No. 61732004, 61732022, 61672020).

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. An, B., Chen, B., Han, X., Sun, L.: Accurate text-enhanced knowledge graph representation learning. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 745–755 (2018)

    Google Scholar 

  2. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge, pp. 1247–1250 (2008)

    Google Scholar 

  3. Bordes, A., Usunier, N., Garciaduran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data, pp. 2787–2795 (2013)

    Google Scholar 

  4. Cai, L., Wang, W.Y.: Kbgan: adversarial learning for knowledge graph embeddings. arXiv preprint arXiv:1711.04071 (2017)

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

    Google Scholar 

  6. Devlin, J., Chang, M., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. \({\rm ar}\)\({\rm Xiv}\): Computation and Language (2018)

    Google Scholar 

  7. Elsahar, H., et al.: A large scale alignment of natural language with knowledge base triples, T-rex (2018)

    Google Scholar 

  8. Godbole, A., Kavarthapu, D., Das, R., Gong, Z., McCallum, A.: Multi-step entity-centric information retrieval for multi-hop question answering

    Google Scholar 

  9. Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 687–696 (2015)

    Google Scholar 

  10. Ji, G., Liu, K., He, S., Zhao, J.: Knowledge graph completion with adaptive sparse transfer matrix. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)

    Google Scholar 

  11. Jin, J., Luo, J., Khemmarat, S., Dong, F., Gao, L.: GSTAR: an efficient framework for answering top-k star queries on billion-node knowledge graphs. World Wide Web 22(4), 1611–1638 (2019)

    Article  Google Scholar 

  12. Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: a lite bert for self-supervised learning of language representations

    Google Scholar 

  13. 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)

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

    Google Scholar 

  15. Liu, W., et al.: K-Bert: enabling language representation with knowledge graph. arXiv preprint arXiv:1909.07606 (2019)

  16. Liu, Y., et al.: Roberta: A robustly optimized Bert pretraining approach

    Google Scholar 

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

    Article  Google Scholar 

  18. Nguyen, D.Q., Nguyen, D.Q., Nguyen, T.D., Phung, D.: A convolutional neural network-based model for knowledge base completion and its application to search personalization. Semant. Web 10(5), 947–960 (2019)

    Article  Google Scholar 

  19. Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.: A novel embedding model for knowledge base completion based on 22 convolutional neural network

    Google Scholar 

  20. Nickel, M., Tresp, V., Kriegel, H.-P.: A three-way model for collective learning on multi-relational data. ICML 11, 809–816 (2011)

    Google Scholar 

  21. Peters, M.E., et al.: Knowledge enhanced contextual word representations, pp. 43–54 (2019)

    Google Scholar 

  22. Poerner, N., Waltinger, U., Schütze, H.: Bert is not a knowledge base (yet): Factual knowledge vs. name-based reasoning in unsupervised QA

    Google Scholar 

  23. Reimers, N., Schiller, B., Beck, T., Daxenberger, J., Gurevych, I.: Classification and clustering of arguments with contextualized word embeddings. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019)

    Google Scholar 

  24. 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 

  25. Shi, B., Weninger, T.: Proje: embedding projection for knowledge graph completion. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  26. Soares, L.B., FitzGerald, N., Ling, J., Kwiatkowski, T.: Matching the blanks: distributional similarity for relation learning

    Google Scholar 

  27. 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 

  28. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge, pp. 697–706 (2007)

    Google Scholar 

  29. Sun, Z., Deng, Z.-H., Nie, J.-Y., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)

  30. 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 

  31. Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction (2016)

    Google Scholar 

  32. Wang, H., Kulkarni, V., Wang, W.Y.: Dolores: deep contextualized knowledge graph embeddings. arXiv preprint arXiv:1811.00147 (2018)

  33. 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 

  34. Wang, R., et al.: K-adapter: infusing knowledge into pre-trained models with adapters. \({\rm ar}\)\({\rm Xiv}\): Computation and Language (2020)

    Google Scholar 

  35. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)

    Google Scholar 

  36. Wu, T., et al.: Knowledge graph construction from multiple online encyclopedias. World Wide Web, pp. 1–28 (2019)

    Google Scholar 

  37. Xiao, H., Huang, M., Meng, L., Zhu, X.: SSP: semantic space projection for knowledge graph embedding with text descriptions. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  38. Xiao, H., Huang, M., Zhu, X.: SSP: semantic space projection for knowledge graph embedding with text descriptions. \({\rm ar}\)\({\rm Xiv}\): Computation and Language (2016)

    Google Scholar 

  39. Xiao, H., Huang, M., Zhu, X.: Transg: a generative model for knowledge graph embedding. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2316–2325 (2016)

    Google Scholar 

  40. Xie, R., Liu, Z., Sun, M.: Representation learning of knowledge graphs with hierarchical types, pp. 2965–2971 (2016)

    Google Scholar 

  41. Yang, W., et al.: End-to-end open-domain question answering with Bertserini

    Google Scholar 

  42. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XlNet: generalized autoregressive pretraining for language understanding

    Google Scholar 

  43. Yao, L., Mao, C., Luo, Y.: KG-Bert: Bert for knowledge graph completion

    Google Scholar 

  44. Zhang, Z., Zhuang, F., Qu, M., Lin, F., He, Q.: Knowledge graph embedding with hierarchical relation structure. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3198–3207 (2018)

    Google Scholar 

  45. Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., Liu, Q.: Ernie: enhanced language representation with informative entities, pp. 1441–1451 (2019)

    Google Scholar 

  46. Zhu, H., Dong, L., Wei, F., Wang, W., Qin, B., Liu, T.: Learning to ask unanswerable questions for machine reading comprehension

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, China

    Han Yu, Rong Jiang, Bin Zhou & Aiping Li

Authors
  1. Han Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rong Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aiping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aiping Li .

Editor information

Editors and Affiliations

  1. VU Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  2. VU Amsterdam, Amsterdam, The Netherlands

    Wouter Beek

  3. Victoria University, Melbourne, VIC, Australia

    Hua Wang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Melbourne, VIC, Australia

    Yanchun Zhang

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

Yu, H., Jiang, R., Zhou, B., Li, A. (2020). Knowledge-Infused Pre-trained Models for KG Completion. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-62005-9_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62004-2

  • Online ISBN: 978-3-030-62005-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation