Skip to main content

Advertisement

Springer Nature Link
Log in

A knowledge inference model for question answering on an incomplete knowledge graph

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Question answering over knowledge graph (KGQA) is a task to solve natural language questions on knowledge graphs (KGs). Multi-hop KGQA requires multi-steps reasoning on the KG to find the correct answers to complex questions. However, it is difficult to find the triple required by the question directly when solving complex multi-hop questions for KGs with missing links. To mitigate this challenge, we propose an effective reasoning model that fuses neighbor interaction and a relation recognition module for multi-hop QA. Specifically, we adopt neighbor interaction networks to learn a better entity representation. The model identifies the relations contained in the questions through neural networks to further precisely determine the range of answers. Our method selectively captures the complex hidden information within the KG and overcomes the limitation of the answer range. It can perform well without the help of additional text corpora. The experimental results on two datasets show that our model can effectively capture richer semantic information for reasoning and achieve better results than all baseline models.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Vakulenko S, Longpre S, Tu Z, Anantha R (2021) Question rewriting for conversational question answering. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 355–363

  2. Pota M, Fujita H (2020) Best practices of convolutional neural networks for question classification. Appl Sci 10(14):4710

    Article  Google Scholar 

  3. Esposito M, Damiano E, Minutolo A, De Pietro G, Fujita H (2020) Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering. Inf Sci 514:88–105

    Article  Google Scholar 

  4. Li Z, Zhong Q, Yang J, Duan Y, Wang W, Wu C, He K (2021) Deepkg: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications. Bioinformatics

  5. Huang Y, Yang M (2021) Breadth first reasoning graph for multi-hop question answering. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies. Association for Computational Linguistics, pp 5810–5821

  6. Sun Q, Li W (2020) Rukbc-qa: a framework for question answering over incomplete kbs enhanced with rules injection. In: CCF International conference on natural language processing and Chinese computing. Springer, pp 82–94

  7. Abdelaziz I, Ravishankar S, Kapanipathi P, Roukos S, Gray A G (2021) A semantic parsing and reasoning-based approach to knowledge base question answering. In: Proceedings of the AAAI conference on artificial intelligence, pp 15985–15987

  8. Kaiser M, Saha Roy R, Weikum G (2021) Reinforcement learning from reformulations in conversational question answering over knowledge graphs. In: 44th International ACM SIGIR conference on research and development in information retrieval. ACM

  9. Xu F, Xu G, Wang Y, Wang R, Ding Q, Liu P, Zhu Z (2022) Diverse dialogue generation by fusing mutual persona-aware and self-transferrer. Appl Intell 52(5):4744–4757

    Article  Google Scholar 

  10. Sun H, Bedrax-Weiss T, Cohen W (2019a) Pullnet: open domain question answering with iterative retrieval on knowledge bases and text. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 2380–2390

  11. Sun Z, Deng Z, Nie J, Tang J (2019b) Rotate: knowledge graph embedding by relational rotation in complex space. In: ICLR 2019, Openreview.net

  12. Saxena A, Tripathi A, Talukdar P P (2020) Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: ACL 2020, Online, July 5–10, 2020. Association for Computational Linguistics, pp 4498–4507

  13. Velickovic P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2017) Graph attention networks. CoRR. arXiv:1710.10903

  14. Nathani D, Chauhan J, Sharma C, Kaul M (2019) Learning attention-based embeddings for relation prediction in knowledge graphs. In: ACL 2019, Florence, Italy, July 28–August 2, 2019, volume 1: long papers. Association for Computational Linguistics, pp 4710–4723

  15. Zhang M, Zhang R, Zou L, Lin Y, Hu S (2021) NAMER: A node-based multitasking framework for multi-hop knowledge base question answering. In: NAACL-HLT 2021, Online, June 6–11, 2021. Association for Computational Linguistics, pp 18–25

  16. Shen T, Geng X, Qin T, Guo D, Tang D, Duan N, Long G, Jiang D (2019) Multi-task learning for conversational question answering over a large-scale knowledge base. In: EMNLP-IJCNLP 2019, Hong Kong, China, November 3–7, 2019. Association for Computational Linguistics, pp 2442–2451

  17. Ge D, Li J, Zhu M (2019) A transformer-based semantic parser for NLPCC-2019 shared task 2. In: NLPCC 2019, Dunhuang, China, October 9-14, 2019, proceedings, Part II, Springer, Lecture Notes in Computer Science, vol 11839, pp 772–781

  18. Undorf M, Livneh I, Ackerman R (2021) Metacognitive control processes in question answering: help seeking and withholding answers. Metacognition and Learning

  19. Ma T, Lv S, Huang L, Hu S (2021) Hiam: a hierarchical attention based model for knowledge graph multi-hop reasoning. Neural Netw

  20. Sun H, Dhingra B, Zaheer M, Mazaitis K, Salakhutdinov R, Cohen W W (2018) Open domain question answering using early fusion of knowledge bases and text. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31–November 4, 2018. Association for Computational Linguistics, pp 4231–4242

  21. Kipf T N, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: ICLR 2017, Toulon, France, April 24–26, 2017, Conference Track Proceedings, OpenReview.net

  22. Yasunaga M, Ren H, Bosselut A, Liang P, Leskovec J (2021) QA-GNN: reasoning with language models and knowledge graphs for question answering. In: NAACL-HLT 2021, Online, June 6–11, 2021. Association for Computational Linguistics, pp 535– 546

  23. Cao Y, Fang M, Tao D (2019) BAG: bi-directional attention entity graph convolutional network for multi-hop reasoning question answering. In: NAACL-HLT 2019, Minneapolis, MN, USA, June 2–7, 2019, vol 1 (Long and Short Papers). Association for Computational Linguistics, pp 357–362

  24. Lv S, Guo D, Xu J, Tang D, Duan N, Gong M, Shou L, Jiang D, Cao G, Hu S (2020) Graph-based reasoning over heterogeneous external knowledge for commonsense question answering. In: Proceedings of the AAAI conference on artificial intelligence, pp 8449–8456

  25. Bordes A, Usunier N, García-Durán A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013. December 5–8, 2013, Lake Tahoe, Nevada, United States, pp 2787–2795

  26. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: AAAI 2014 July 27–31, 2014, Québec City, QuéBec, Canada. AAAI Press, pp 1112–1119

  27. Nickel M, Tresp V, Kriegel H (2011) A three-way model for collective learning on multi-relational data. In: ICML 2011, Bellevue, Washington, USA, June 28–July 2, 2011. Omnipress, pp 809–816

  28. Yang M, Lee D, Park S, Rim H (2015) Knowledge-based question answering using the semantic embedding space. Expert Syst Appl 42(23):9086–9104

    Article  Google Scholar 

  29. Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G (2016) Complex embeddings for simple link prediction. In: ICML 2016, New York City, NY, USA, June 19–24, 2016, JMLR.org, JMLR Workshop and Conference Proceedings, vol 48, pp 2071–2080

  30. Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724–2743

    Article  Google Scholar 

  31. Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. In: AAAI 2018, New Orleans, Louisiana, USA, February 2–7, 2018. AAAI Press, pp 1811–1818

  32. Nguyen D Q, Nguyen T D, Nguyen D Q, Phung D Q (2018) A novel embedding model for knowledge base completion based on convolutional neural network. In: NAACL-HLT, New Orleans, Louisiana, USA, June 1–6, 2018, vol 2 (Short Papers). Association for Computational Linguistics, pp 327–333

  33. Zeiler M D, Fergus R (2014) Visualizing and understanding convolutional networks. In: Computer vision—ECCV 2014—13th European conference, Zurich, Switzerland, September 6–12, 2014, proceedings, Part I, Springer, Lecture Notes in Computer Science, vol 8689, pp 818–833

  34. Yao L, Mao C, Luo Y (2019) KG-BERT: BERT For knowledge graph completion. CoRR arXiv:1909.03193

  35. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT 2019, Minneapolis, MN, USA, June 2–7, 2019, vol 1 (Long and Short Papers). Association for Computational Linguistics, pp 4171–4186

  36. Schlichtkrull M S, Kipf T N, Bloem P, van den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018. Springer, lecture notes in computer science, vol 10843, pp 593–607

  37. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv:190711692

  38. Zhang Y, Dai H, Kozareva Z, Smola A J, Song L (2018) Variational reasoning for question answering with knowledge graph. In: AAAI 2018, New Orleans, louisiana, USA, February 2–7, 2018. AAAI Press, pp 6069–6076

  39. Yih W, Richardson M, Meek C, Chang M, Suh J (2016) The value of semantic parse labeling for knowledge base question answering. In: ACL 2016, August 7–12, 2016, Berlin, Germany, volume 2: short papers. The Association for Computer Linguistics

  40. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data. ACM, pp 1247–1250

  41. Miller A H, Fisch A, Dodge J, Karimi A, Bordes A, Weston J (2016) Key-value memory networks for directly reading documents. In: EMNLP 2016, Austin, Texas, USA, November 1–4, 2016. The Association for Computational Linguistics, pp 1400–1409

Download references

Acknowledgements

This work was supported in part by the National Social Science Foundation under Award 19BYY076, in part Key R & D project of Shandong Province 2019JZZY010129, and in part by the Shandong Provincial Social Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award 18BJYJ04.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250357, China

    Qimeng Guo, Xue Wang, Peiyu Liu & Liancheng Xu

  2. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, 250357, China

    Zhenfang Zhu

Authors
  1. Qimeng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenfang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peiyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liancheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Peiyu Liu or Liancheng Xu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Q., Wang, X., Zhu, Z. et al. A knowledge inference model for question answering on an incomplete knowledge graph. Appl Intell 53, 7634–7646 (2023). https://doi.org/10.1007/s10489-022-03927-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03927-0

Keywords

Search

Navigation