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International Conference on Neural Information Processing

ICONIP 2021: Neural Information Processing pp 489–500Cite as

Improving Question Answering over Knowledge Graphs Using Graph Summarization

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 13111)

Abstract

Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural language questions using triples contained in a KG. The key idea is to represent questions and entities of a KG as low-dimensional embeddings. Previous KGQAs have attempted to represent entities using Knowledge Graph Embedding (KGE) and Deep Learning (DL) methods. However, KGEs are too shallow to capture the expressive features and DL methods process each triple independently. Recently, Graph Convolutional Network (GCN) has shown to be excellent in providing entity embeddings. However, using GCNs to KGQAs is inefficient because GCNs treat all relations equally when aggregating neighbourhoods. Also, a problem could occur when using previous KGQAs: in most cases, questions often have an uncertain number of answers. To address the above issues, we propose a graph summarization technique using Recurrent Convolutional Neural Network (RCNN) and GCN. The combination of GCN and RCNN ensures that the embeddings are propagated together with the relations relevant to the question, and thus better answers. The proposed graph summarization technique can be used to tackle the issue that KGQAs cannot answer questions with an uncertain number of answers. In this paper, we demonstrated the proposed technique on the most common type of questions, which is single-relation questions. Experiments have demonstrated that the proposed graph summarization technique using RCNN and GCN can provide better results when compared to the GCN. The proposed graph summarization technique significantly improves the recall of actual answers when the questions have an uncertain number of answers.

Keywords

  • Question answering
  • Knowledge graph
  • Graph summarization
  • Graph convolutional network
  • Recurrent convolutional neural network

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Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://spacy.io/.

  2. 2.

    https://research.fb.com/downloads/babi/.

  3. 3.

    https://www.microsoft.com/en-us/download/details.aspx?id=52763.

  4. 4.

    https://github.com/zmtkeke/IRN.

  5. 5.

    https://github.com/davidgolub/SimpleQA/tree/master/datasets/SimpleQuestions.

  6. 6.

    https://developers.google.com/freebase.

References

  1. Aghaebrahimian, A.: Hybrid deep open-domain question answering. In: Proceedings of the 8th Language and Technology Conference (LTC) (2017)

    Google Scholar 

  2. Arora, S.: A survey on graph neural networks for knowledge graph completion. arXiv preprint arXiv:2007.12374 (2020)

  3. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 1–9 (2013)

    Google Scholar 

  4. De Cao, N., Aziz, W., Titov, I.: Question answering by reasoning across documents with graph convolutional networks. arXiv preprint arXiv:1808.09920 (2018)

  5. Deng, L., Liu, Y.: Deep Learning in Natural Language Processing. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5209-5

  6. Goh, O.S., Wong, K.W., Fung, C.C., Depickere, A.: Towards a more natural and intelligent interface with embodied conversation agent (2006)

    Google Scholar 

  7. Huang, X., Zhang, J., Li, D., Li, P.: Knowledge graph embedding based question answering. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 105–113. ACM (2019)

    Google Scholar 

  8. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  9. Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  10. Li, D., et al.: TSPNet: hierarchical feature learning via temporal semantic pyramid for sign language translation. In: Advances in Neural Information Processing Systems, vol. 33 (2020)

    Google Scholar 

  11. Li, D., Yu, X., Xu, C., Petersson, L., Li, H.: Transferring cross-domain knowledge for video sign language recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6205–6214 (2020)

    Google Scholar 

  12. Li, X., Alazab, M., Li, Q., Yu, K., Yin, Q.: Question-aware memory network for multi-hop question answering in human-robot interaction. arXiv preprint arXiv:2104.13173 (2021)

  13. Li, Z., Liu, H., Zhang, Z., Liu, T., Xiong, N.N.: Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans. Neural Netw. Learn. Syst., 1–13 (2021). https://doi.org/10.1109/TNNLS.2021.3055147

  14. Liu, Y., et al.: Invertible denoising network: a light solution for real noise removal. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13365–13374 (2021)

    Google Scholar 

  15. Miller, A., Fisch, A., Dodge, J., Karimi, A.H., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents. arXiv preprint arXiv:1606.03126 (2016)

  16. Qiu, Y., Wang, Y., Jin, X., Zhang, K.: Stepwise reasoning for multi-relation question answering over knowledge graph with weak supervision. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 474–482 (2020)

    Google Scholar 

  17. Saxena, A., Tripathi, A., Talukdar, P.: Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4498–4507 (2020)

    Google Scholar 

  18. Shi, J., Cao, S., Hou, L., Li, J., Zhang, H.: TransferNet: an effective and transparent framework for multi-hop question answering over relation graph. arXiv preprint arXiv:2104.07302 (2021)

  19. Sun, H., Bedrax-Weiss, T., Cohen, W.W.: PullNet: open domain question answering with iterative retrieval on knowledge bases and text. arXiv preprint arXiv:1904.09537 (2019)

  20. Sun, H., Dhingra, B., Zaheer, M., Mazaitis, K., Salakhutdinov, R., Cohen, W.W.: Open domain question answering using early fusion of knowledge bases and text. arXiv preprint arXiv:1809.00782 (2018)

  21. Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080. PMLR (2016)

    Google Scholar 

  22. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)

    Google Scholar 

  23. Xie, Z., Zhou, G., Liu, J., Huang, X.: ReInceptionE: relation-aware inception network with joint local-global structural information for knowledge graph embedding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5929–5939 (2020)

    Google Scholar 

  24. Zhang, J., Pei, Z., Xiong, W., Luo, Z.: Answer extraction with graph attention network for knowledge graph question answering. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 1645–1650. IEEE (2020)

    Google Scholar 

  25. Zhu, D., Wong, K.W.: An evaluation study on text categorization using automatically generated labeled dataset. Neurocomputing 249, 321–336 (2017)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. Discipline of Information Technology, Murdoch University, South Street, Murdoch, Western Australia

    Sirui Li, Kok Wai Wong, Chun Che Fung & Dengya Zhu

  2. Discipline of Business Information Systems, School of Management and Marketing, Curtin University, Kent Street, Bentley, Western Australia

    Dengya Zhu

Authors
  1. Sirui Li
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  2. Kok Wai Wong
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  3. Chun Che Fung
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  4. Dengya Zhu
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Corresponding author

Correspondence to Sirui Li .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Dr. Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Dr. Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Dr. Achmad Nizar Hidayanto

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Li, S., Wong, K.W., Fung, C.C., Zhu, D. (2021). Improving Question Answering over Knowledge Graphs Using Graph Summarization. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_40

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  • DOI: https://doi.org/10.1007/978-3-030-92273-3_40

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