Skip to main content
SpringerLink
Log in

Multi-hop question answering over incomplete knowledge graph with abstract conceptual evidence

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Multi-hop Question Answering over Knowledge Graph (KGQA) aims to reason answers through multiple triples in Knowledge Graphs (KGs). Unfortunately, in practice due to the rapid growth of information, KGs are often incomplete with missing relations, increasing the difficulty of reasoning for multi-hop KGQA. Most prior works utilize auxiliary text corpus enhancement to tackle the challenge of incomplete KG. However, they fail to consider the fine-grained semantic structure contained in texts, which would be critical to reveal the missing relations of KGs. Meanwhile, they ignore semantic associations among multiple texts, leading to the blocking of the reasoning process along texts. To address these problems, we present the idea of extracting question-candidate evidences and parsing them into Abstract Conceptual Evidences (ACEs) to explicitly capture fine-grained semantic information. Accordingly, we propose an Abstract Conceptual Evidence Reasoning (ACER) model, exploiting the semantic associations among ACEs to form multi-hop evidence reasoning paths from the question to candidate answers. Moreover, a Statement-Evidence Reasoning method is introduced to further aggregate evidences and a joint entity scoring method for answer selection. Extensive experiments demonstrate the effectiveness of the ACER by its outperformance on different extents of KG incompleteness, as well as its interpretability of the answer reasoning process.

Graphical abstract

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data Availability

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

Notes

  1. The term document will always refer to a sentence in this paper.

References

  1. Pillai Sini Govinda, Soon Lay-Ki, Haw Su-Cheng (2019) Comparing DBpedia, Wikidata, and YAGO for web information retrieval. Intelligent and Interactive Computing: Proceedings of IIC 2018:525–535. https://doi.org/10.1016/j.websem.100679

    Article  Google Scholar 

  2. Shenoy Kartik, Ilievski Filip, Garijo Daniel, Schwabe Daniel, Szekely Pedro (2022) A study of the quality of wikidata. Journal of Web Semantics 72:100–679. https://doi.org/10.3390/electronics9050750

    Article  Google Scholar 

  3. Md. Rashad Al Hasan Rony and Debanjan Chaudhuri and Ricardo Usbeck and Jens Lehmann (2022) Tree-KGQA: An Unsupervised Approach for Question Answering Over Knowledge Graphs, IEEE Access, 10:50, 467–50,478. https://doi.org/10.1109/ACCESS.2022.3173355

  4. Do Phuc, Phan Truong HV (2022) Developing a BERT based triple classification model using knowledge graph embedding for question answering system. Appl Intell 52(1):636–651. https://doi.org/10.1007/s10489-021-02460-w

    Article  Google Scholar 

  5. Etezadi, Romina and Shamsfard, Mehrnoush (2022) The state of the art in open domain complex question answering: a survey, Appl Intell pp 1–21. https://doi.org/10.1007/s10489-022-03732-9

  6. Roy Rishiraj Saha, Anand Avishek (2022) Multi-Hop Question Answering. Question Answering for the Curated Web 121–128. https://doi.org/10.1007/978-3-031-79512-1_11

  7. Cao Xing, Liu Yun (2022) Coarse-grained decomposition and fine-grained interaction for multi-hop question answering. Journal of Intelligent Information Systems 58(1):21–41. https://doi.org/10.1007/s10844-021-00645-w

    Article  Google Scholar 

  8. Zhao Fen, Li Yinguo, Hou Jie, Bai Ling (2022) Improving question answering over incomplete knowledge graphs with relation prediction. Neural Computing and Applications 34(8):6331–6348. https://doi.org/10.1007/s00521-021-06736-7

    Article  Google Scholar 

  9. Lan Yunshi, He Gaole, Jiang Jinhao, Jiang Jing, Zhao Wayne Xin, Wen Ji-Rong (2022) Complex knowledge base question answering: A survey. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2022.3223858

    Article  Google Scholar 

  10. Guo Qimeng, Wang Xue, Zhu Zhenfang, Liu Peiyu, Xu, (2022) Liancheng A knowledge inference model for question answering on an incomplete knowledge graph. Appl Intell 1–13. https://doi.org/10.1007/s10489-022-03927-0

  11. Saxena, Apoorv and Tripathi, Aditay and Talukdar, Partha (2020) Improving multi-hop question answering over knowledge graphs using knowledge base embeddings, The 58th annual meeting of ACL, 4498–4507. https://doi.org/10.18653/v1/2020.acl-main.412

  12. Saxena, Apoorv and Kochsiek, Adrian and Gemulla, Rainer (2022) Sequence-to-Sequence Knowledge Graph Completion and Question Answering, ACL 2022, 2814–2828.https://doi.org/10.18653/v1/2022.acl-long.201

  13. Sun, Haitian and Dhingra, Bhuwan and Zaheer, Manzil and Mazaitis, Kathryn and Salakhutdinov, Ruslan and Cohen, William (2018) Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text, EMNLP pp 4231–4242. https://doi.org/10.18653/v1/D18-1455

  14. Sun, Haitian and Bedrax-Weiss, Tania and Cohen, William (2019) PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text, EMNLP-IJCNLP,pp 2380–2390.https://doi.org/10.18653/v1/D19-1242

  15. Xiong, Wenhan and Yu, Mo and Chang, Shiyu and Guo, Xiaoxiao and Wang, William Yang (2019) Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader The 57th Annual Meeting of the ACL, 4258–4264, https://doi.org/10.18653/v1/P19-1417

  16. Han, Jiale and Cheng, Bo and Wang, Xu (2020) Open domain question answering based on text enhanced knowledge graph with hyperedge infusion, Findings of the Association for Computational Linguistics: EMNLP 2020, pp 1475–1481. https://doi.org/10.18653/v1/2020.findings-emnlp.133

  17. Chen, Qian and Gao, Xiaoying and Guo, Xin and Wang, Suge (2022) Deep Structure-Aware Approach for QA Over Incomplete Knowledge Bases. In: Natural Language Processing and Chinese Computing, 2022, Proceedings, Part I, pp 837–849. https://doi.org/10.1007/978-3-031-17120-8_64

  18. Zhang Jinhao, Zhang Lizong, Hui Bei, Tian Ling (2022) Improving complex knowledge base question answering via structural information learning. Knowledge-Based Systems 242:108–252. https://doi.org/10.1016/j.knosys.2022.108252

  19. Cui H, Peng T, Bao T, et al (2022) Stepwise relation prediction with dynamic reasoning network for multi-hop knowledge graph question answering. Appl Intell pp 1–15. https://doi.org/10.1007/s10489-022-04127-6

  20. Chen H, Ye F, Fan Y et al (2022) Staged query graph generation based on answer type for question answering over knowledge base. Knowledge-Based Systems 253(109):576. https://doi.org/10.1016/j.knosys.2022.109576

    Article  Google Scholar 

  21. Wu W, Zhu Z, Qi J et al (2023) A dynamic graph expansion network for multi-hop knowledge base question answering. Neurocomputing 515:37–47. https://doi.org/10.1016/j.neucom.2022.10.023

    Article  Google Scholar 

  22. Lv S, Guo D, Xu J, et al (2020) Graph-based reasoning over heterogeneous external knowledge for commonsense question answering. In: The AAAI Conference on Artificial Intelligence, pp 8449-8456. https://doi.org/10.1609/aaai.v34i05.6364

  23. Wang R,Wang M, Liu J, et al (2019) Leveraging knowledge graph embeddings for natural language question answering. In: Database Systems for Advanced Applications, 2019, Proceedings, Part I 24, Springer, pp 659-675. https://doi.org/10.1007/978-3-030-18576-339

  24. Cai J, Zhang Z, Wu F, et al (2021) Deep cognitive reasoning network for multi-hop question answering over knowledge graphs. In: ACL-IJCNLP, pp 219–229

  25. He G, Lan Y, Jiang J, et al (2021) Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In: The 14th ACM International Conference on Web Search and Data Mining, pp 553–561. https://doi.org/10.1145/3437963.3441753

  26. Bi X, Nie H, Zhang X et al (2022) Unrestricted multi-hop reasoning network for interpretable question answering over knowledge graph. Knowledge-Based Systems 243(108):515. https://doi.org/10.1016/j.knosys.2022.108515

    Article  Google Scholar 

  27. Dai Y, Wang S, Xiong NN et al (2020) A survey on knowledge graph embedding: Approaches, applications and benchmarks. Electronics. 9(5):750. https://doi.org/10.3390/electronics9050750

    Article  Google Scholar 

  28. Le T, Huynh N, Le B (2022) Knowledge graph embedding by projection and rotation on hyperplanes for link prediction. Applied Intelligence pp 1–25. https://doi.org/10.1007/s10489-022-03983-6

  29. Ahmadvand M, Tahmoresnezhad J (2021) Metric transfer learning via geometric knowledge embedding. Applied Intelligence 51(2):921–934. https://doi.org/10.1007/s10489-020-01853-7

    Article  Google Scholar 

  30. Miller A, Fisch A, Dodge J, et al (2016) Key-value memory networks for directly reading documents. In: EMNLP 2016, pp 1400–1409. https://doi.org/10.18653/v1/D16-1147

  31. Shi J, Cao S, Hou L, et al (2021) Transfernet: An effective and transparent framework for multi-hop question answering over relation graph. In: The 2021 Conference on Empirical Methods in Natural Language Processing, pp 4149–4158. https://doi.org/10.18653/v1/2022.acl-long.422

  32. Oguz B, Chen X, Karpukhin V, et al (2022) Unik-qa: Unified representations of structured and unstructured knowledge for open-domain question answering. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp 1535–1546. https://doi.org/10.18653/v1/2022.findings-naacl.115

  33. Liu L, Du B, Xu J, et al (2022) Joint knowledge graph completion and question answering. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 1098–1108. https://doi.org/10.1145/3534678.3539289

  34. Cui H, Peng T, Han R, et al (2023) Reinforcement learning with dynamic completion for answering multi-hop questions over incomplete knowledge graph. Information Processing & Management 6 (3):103,283. https://doi.org/10.1016/j.ipm.2023.103283

  35. Wang P, Wu Q, Shen C et al (2017) Fvqa: Fact-based visual question answering. IEEE transactions on pattern analysis and machine intelligence 40(10):2413–2427. https://doi.org/10.1109/TPAMI.2017.2754246

    Article  Google Scholar 

  36. Garcia N, Otani M, Chu C, et al (2020) Knowit vqa: Answering knowledge-based questions about videos. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 10,826–10,834. https://doi.org/10.1609/aaai.v34i07.6713

  37. Wu T, Garcia N, Otani M, et al (2021) Transferring domain-agnostic knowledge in video question answering. BMVC 2021

  38. Song L, Li J, Liu J et al (2023) Answering knowledge-based visual questions via the exploration of question purpose. Pattern Recognition 133(109):015. https://doi.org/10.1016/j.patcog.2022.109015

  39. Chowdhury S, Soni B (2023) Qsfvqa: A time efficient, scalable and optimized vqa framework. Arabian Journal for Science and Engineering pp 1–13. https://doi.org/10.1007/s13369-023-07661-8

  40. Banarescu L, Bonial C, Cai S, et al (2013) Abstract meaning representation for sembanking. In: The 7th linguistic annotation workshop and interoperability with discourse, pp 178–186

  41. Shou Z, Jiang Y, Lin F (2022) Amr-da: Data augmentation by abstract meaning representation. In: Findings of the Association for Computational Linguistics: ACL 2022, pp 3082–3098. https://doi.org/10.18653/v1/2022.findings-acl.244

  42. Bonial C, Donatelli L, Abrams M, et al (2020) Dialogue-amr: abstract meaning representation for dialogue. In: The 12th Language Resources and Evaluation Conference, pp 684–695

  43. Deng Z, Zhu Y, Chen Y, et al (2022) Interpretable amr-based question decomposition for multi-hop question answering. IJCAI 2022

  44. Xu W, Zhang H, Cai D et al (2021) Dynamic semantic graph construction and reasoning for explainable multi-hop science question answering. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021:1044–1056

    Google Scholar 

  45. Xu W, Deng Y, Zhang H, et al (2021) Exploiting reasoning chains for multi-hop science question answering. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp 2814–2828. https://doi.org/10.18653/v1/2021.findings-emnlp.99

  46. Trouillon T, Welbl J, Riedel S, et al (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, PMLR, pp 2071–2080

  47. Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. In: EMNLP-IJCNLP 2019, pp 3982-3992. https://doi.org/10.18653/v1/D19-1410

  48. Chen D, Fisch A, Weston J, et al (2017) Reading wikipedia to answer open-domain questions. In: The 55th Annual Meeting of the ACL, pp 1870–1879. https://doi.org/10.18653/v1/P17-1171

  49. Roy RS, Anand A (2022) Answering over heterogeneous sources. In: Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections. Springer, p 53–63. https://doi.org/10.1007/978-3-031-79512-15

  50. Cai D, Lam W (2020) Amr parsing via graph-sequence iterative inference. In: The 58th Annual Meeting of the ACL, pp 1290–1301. https://doi.org/10.18653/v1/2020.acl-main.119

  51. Liu Y, Ott M, Goyal N, et al (2020) Roberta: A robustly optimized bert pretraining approach. https://openreview.net/forum? id=SyxS0T4tvS

  52. Velickovic P, Cucurull G, Casanova A, et al (2018) Graph attention networks. In: ICLR 2018, https://openreview.net/forum?id=rJXMpikCZ

  53. Zhang Y, Dai H, Kozareva Z, et al (2018) Variational reasoning for question answering with knowledge graph. In: The 32th AAAI conference on artificial intelligence, https://doi.org/10.1609/aaai.v32i1.12057

  54. Yih Wt, Richardson M, Meek C, et al (2016) The value of semantic parse labeling for knowledge base question answering. In: The 54th Annual Meeting of the ACL, pp 201–206.https://doi.org/10.18653/v1/P16-2033

  55. Wu Z, Pan S, Chen F et al (2020) A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386

    Article  MathSciNet  Google Scholar 

  56. Lai G, Xie Q, Liu H, et al (2017) Race: Large-scale reading comprehension dataset from examinations. In: EMNLP 2017, pp 785–794. https://doi.org/10.18653/v1/D17-1082

  57. Müller A, Kuwertz A (2022) Evaluation of a semantic search approach based on amr for information retrieval in image exploitation. In: 2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF), IEEE, pp 1–6. https://doi.org/10.1109/SDF55338.2022.9931702

Download references

Acknowledgements

This work is supported by the National Key R &D Program of China under Grant Agreement No 2019YFF0302601 and Key-Area Research and Development Program of Guangdong Province under Grant Agreement No 2020B0101130013

Author information

Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qi Sun & Zheng Hu

  2. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Chunhong Zhang, Zhihong Jin, Jibin Yu & Liping Liu

Authors
  1. Qi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunhong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhihong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jibin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Qi Sun: Conceptualization, Methodology, Software, Writing - original draft. Chunhong Zhang: Conceptualization, Supervision, Writing - review & editing. Zheng Hu: Validation, Funding acquisition, Resources. Zhihong Jin: Software, Validation. Jibin Yu: Investigation, Writing - review & editing. Liping Liu: Data Curation, Writing review & editing.

Corresponding author

Correspondence to Chunhong Zhang.

Ethics declarations

Ethical and informed consent for data used

The data used in this paper are from publicly available datasets and do not violate any ethical guidelines.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Q., Zhang, C., Hu, Z. et al. Multi-hop question answering over incomplete knowledge graph with abstract conceptual evidence. Appl Intell 53, 25731–25751 (2023). https://doi.org/10.1007/s10489-023-04849-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-04849-1

Keywords

Search

Navigation