Skip to main content
SpringerLink
Log in

LeKAN: Extracting Long-tail Relations via Layer-Enhanced Knowledge-Aggregation Networks

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13245))

Included in the following conference series:

Abstract

Long-tailed relation extraction is a crucial task in the information extraction field for extracting the long-tailed, imbalanced relation between two annotated entities based on related context. Although many works have been devoted to distinguishing valid instances from noisy data and have achieved promising performance, such studies still have critical defects: works based on nonhierarchical relations ignore the correlations among the relations, and those based on hierarchical relations neglect the hierarchy of the relation structure, which is unbalanced and causes difficulty in extracting data-poor classes. In this paper, a novel layer-enhanced knowledge aggregation network, named LeKAN, is presented to classify the relations between two annotated entities from text, especially long-tailed relations, which are very common in various corpora. Inspired by the election mechanism, we aggregate the ancestors of long-tailed relation classes into new relation representations to prevent the long-tailed relations from being ignored. Specifically, we use GraphSAGE to learn the relational knowledge from an existing knowledge graph via class embedding. Moreover, we aggregate the acquired relational knowledge into the LeKAN by layer-enhanced knowledge-aggregating attention mechanism. Comprehensive experimental results demonstrate that the new method yields considerable improvement over other relation extraction methods on a large-scale benchmark dataset with a long-tailed distribution.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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.

    http://iesl.cs.umass.edu/riedel/ecml/.

  2. 2.

    https://github.com/thunlp/OpenNRE

References

  1. Gui, Y., Liu, Q., Zhu, M., Gao, Z.: Exploring long tail data in distantly supervised relation extraction. In: Proceedings of 2016 Natural Language Understanding and Intelligent Applications, pp. 514–522 (2016)

    Google Scholar 

  2. Lei, K., Chen, D., et al.: Cooperative denoising for distantly supervised relation extraction. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 426–436 (2018)

    Google Scholar 

  3. Huang, Y., Zhao, F., Gui, X., Jin, H.: Path-enhanced explainable recommendation with knowledge graphs. World Wide Web 24(5), 1769–1789 (2021). https://doi.org/10.1007/s11280-021-00912-4

    Article  Google Scholar 

  4. Song, S., Huang, Y., Lu, H.: Rumor detection on social media with out-in-degree graph convolutional networks. In: Proceedings of the 2021 IEEE Conference on Systems, Man, and Cybernetics, pp. 2395–2400 (2021)

    Google Scholar 

  5. Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III, pp. 148–163 (2010)

    Google Scholar 

  6. Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3(Feb), 1083–1106 (2003)

    Google Scholar 

  7. Zhou, G., Su, J., Zhang, J., Zhang, M.: Exploring various knowledge in relation extraction. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp. 427–434 (2005)

    Google Scholar 

  8. Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003–1011 (2009)

    Google Scholar 

  9. Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 541–550 (2011)

    Google Scholar 

  10. Ma, R., Gui, T., Li, L., Zhang, Q., Zhou, Y., Huang, X.: Sent: sentence-level distant relation extraction via negative training. arXiv preprint arXiv:2106.11566 (2021)

  11. Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers, pp. 2335–2344 (2014)

    Google Scholar 

  12. Christopoulou, F., Miwa, M., Ananiadou, S.: Distantly supervised relation extraction with sentence reconstruction and knowledge base priors. arXiv preprint arXiv:2104.08225 (2021)

  13. Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)

  14. Zhou, B., Cui, Q., Wei, X.S., Chen, Z.M.: BBN: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In: Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition, pp. 9719–9728 (2020)

    Google Scholar 

  15. Wang, Y., Gan, W., Yang, J., Wu, W., Yan, J.: Dynamic curriculum learning for imbalanced data classification. In: Proceedings of the 2019 IEEE International Conference on Computer Vision, pp. 5017–5026 (2019)

    Google Scholar 

  16. Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, pp. 9268–9277 (2019)

    Google Scholar 

  17. Jamal, M.A., Brown, M., Yang, M.H., Wang, L., Gong, B.: Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective. In: Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition, pp. 7610–7619 (2020)

    Google Scholar 

  18. Jiang, X., Wang, Q., Li, P., Wang, B.: Relation extraction with multi-instance multi-label convolutional neural networks. In: Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1471–1480 (2016)

    Google Scholar 

  19. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2020)

    Article  Google Scholar 

  20. Han, X., Yu, P., Liu, Z., Sun, M., Li, P.: Hierarchical relation extraction with coarse-to-fine grained attention. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2236–2245 (2018)

    Google Scholar 

  21. Zhang, N., et al.: Long-tail relation extraction via knowledge graph embeddings and graph convolution networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 3016–3025 (2019)

    Google Scholar 

  22. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, Vol. 2, pp. 2787–2795 (2013)

    Google Scholar 

  23. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  24. Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1753–1762 (2015)

    Google Scholar 

  25. Lin, Y., Shen, S., Liu, Z., Luan, H., Sun, M.: Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, (Volume 1: Long Papers), pp. 2124–2133 (2016)

    Google Scholar 

  26. Han, X., Liu, Z., Sun, M.: Neural knowledge acquisition via mutual attention between knowledge graph and text. In: Proceedings of the 2018 AAAI Conference on Artificial Intelligence. vol. 32(1), pp. 4832–4839 (2018)

    Google Scholar 

  27. Liu, T., Wang, K., Chang, B., Sui, Z.: A soft-label method for noise-tolerant distantly supervised relation extraction. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1790–1795 (2017)

    Google Scholar 

  28. Feng, J., Huang, M., Zhao, L., Yang, Y., Zhu, X.: Reinforcement learning for relation classification from noisy data. In: Proceedings of the 2018 AAAI Conference on Artificial Intelligence, vol. 32, no. 1, pp. 5779–5786 (2018)

    Google Scholar 

  29. Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 455–465 (2012)

    Google Scholar 

  30. Laurens, V.D.M., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2605), 2579–2605 (2008)

    MATH  Google Scholar 

Download references

Acknowledgment

This work was supported in part by National Key R&D Program of China under Grants No. 2018YFB1404302, National Natural Science Foundation of China under Grants No.62072203.

Author information

Authors and Affiliations

  1. National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, Wuhan, China

    Xiaokai Liu, Feng Zhao, Xiangyu Gui & Hai Jin

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

    Feng Zhao, Xiangyu Gui & Hai Jin

  3. School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Xiaokai Liu

Authors
  1. Xiaokai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangyu Gui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hai Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Zhao .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Kanpur, India

    Arnab Bhattacharya

  2. National University of Singapore, Singapore, Singapore

    Janice Lee Mong Li

  3. University of California, Santa Barbara, Santa Barbara, CA, USA

    Divyakant Agrawal

  4. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  5. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Mukesh Mohania

  6. Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

  7. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Vikram Goyal

  8. University of Aizu, Aizu, Japan

    Rage Uday Kiran

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, X., Zhao, F., Gui, X., Jin, H. (2022). LeKAN: Extracting Long-tail Relations via Layer-Enhanced Knowledge-Aggregation Networks. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-00123-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-00122-2

  • Online ISBN: 978-3-031-00123-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation