Abstract
Knowledge Graph Question Answering (KGQA) is a challenging task that aims to obtain the entities from the given Knowledge Graph (KG) to answer the user’s natural language questions. Most existing studies are focused on the traditional KGQA task, where the test distribution is the same as the training distribution over questions. In contrast, few efforts have been made to explore the zero-shot KGQA task. Logically, the existing models for the traditional KGQA task naturally show poor performance on the zero-shot setting. It is a non-trivial task to migrate the off-the-shelf zero-shot solutions in other common tasks to KGQA since an intrinsical gap exists between other common tasks and the KGQA task under the zero-shot settings. Furthermore, we observed that Similar Questions tend to have Similar Logic forms. Motivated by this, we propose a simple yet effective framework S\(^2\)QL. In detail, we first elaborately devise three similarity measurement units to category the user’s questions. Then based on the Similarity Relation Graph (SRG) constructed by the above similarity measurement units, we devise a retrieval augmented strategy to further answer arduous zero-shot questions with its retrieved similar questions. Extensive experiments on the GrailQA and WebQSP benchmarks demonstrate that our approach is more effective than a number of competitive KGQA baselines on the zero-shot setting.
D. Zan and Y. Yan—Work done during internship at Meituan Inc. The first two authors contribute equally.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Follow the [16], the logical form used is s-expression in our experiment.
- 2.
- 3.
- 4.
References
Annadani, Y., Biswas, S.: Preserving semantic relations for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7603–7612 (2018)
Banerjee, D., Chaudhuri, D., Dubey, M., Lehmann, J.: PNEL: pointer network based end-to-end entity linking over knowledge graphs. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12506, pp. 21–38. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62419-4_2
Bao, J., et al.: Constraint-based question answering with knowledge graph. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, Technical Papers, pp. 2503–2514 (2016)
Bollacker, K., et al.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)
Bordes, A., et al.: Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015)
Chen, J., et al.: Knowledge-aware Zero-shot learning: survey and perspective. arXiv preprint arXiv:2103.00070 (2021)
Chen, S., et al.: ReTraCk: a flexible and efficient framework for knowledge base question answering. In: ACL: System Demonstrations, pp. 325–336 (2021)
Chen, X., Jia, S., Xiang, Y.: A review: knowledge reasoning over knowledge graph. Expert Syst. App. 141, 112948 (2020)
Chen, Z.Y., et al.: UHop: an unrestricted-hop relation extraction framework for knowledge-based question answering. In: ACL: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 345–356 (2019)
Dinu, G., Lazaridou, A., Baroni, M.: Improving zero-shot learning by mitigating the hubness problem. arXiv preprint arXiv:1412.6568 (2014)
Do, T., et al.: Compact trilinear interaction for visual question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 392–401 (2019)
Frome, A., et al.: DeViSE: a deep visual-semantic embedding model. In: NIPS (2013)
Fuglede, B., Topsoe, F.: Jensen-Shannon divergence and Hilbert space embedding. In: Proceedings of International Symposium on Information Theory: ISIT 2004, p. 31. IEEE (2004)
Furlanello, T., et al.: Born again neural networks. In: International Conference on Machine Learning, pp. 1607–1616. PMLR (2018)
Gabrilovich, E., Ringgaard, M., Subramanya, A.: FACC1: Freebase annotation of ClueWeb corpora (2013)
Gu, Y., et al.: Beyond IID: three levels of generalization for question answering on knowledge bases. In: Proceedings of the Web Conference 2021, pp. 3477–3488 (2021)
Guo, Y., et al.: Synthesizing samples from zero-shot learning. In: IJCAI (2017)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
Hayashi, T., Fujita, H.: Cluster-based zero-shot learning for multivariate data. J. Ambient Intell. Human. Comput. 12(2), 1897–1911 (2021)
He, G., et al.: Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 553–561 (2021)
Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
Hu, M., et al.: Attention-guided answer distillation for machine reading comprehension. In: EMNLP, pp. 2077–2086 (2018)
Huang, S., et al.: Learning hypergraph-regularized attribute predictors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–417 (2015)
Kipf ,T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Statist. 22(1), 79–86 (1951)
Lan, Y., Jiang, J.: Query graph generation for answering multi-hop complex questions from knowledge bases. In: Association for Computational Linguistics (2020)
Li, Y., et al.: Zero-shot recognition using dual visual-semantic mapping paths. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3279–3287 (2017)
Nguyen, H.V., Gelli, F., Poria, S.: DOZEN: cross-domain zero shot named entity recognition with knowledge graph. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1642–1646 (2021)
Noh, J., Kavuluru, R.: Joint learning for biomedical NER and entity normalization: encoding schemes, counterfactual examples, and zero-shot evaluation. In: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 1–10 (2021)
Petrochuk, M., Zettlemoyer, L.: Simple questions nearly solved: a new upperbound and baseline approach. In: EMNLP, pp. 554–558 (2018)
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)
Shu, C., et al.: Logic-consistency text generation from semantic parses. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 4414–4426 (2021)
Sun, H., Bedrax-Weiss, T., Cohen, W.: PullNet: open domain question answering with iterative retrieval on knowledge bases and text. In: EMNLP-IJCNLP, pp. 2380–2390 (2019)
Sun, H., et al.: Open domain question answering using early fusion of knowledge bases and text. In: EMNLP, pp. 4231–4242 (2018)
Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions. In: ACL: Human Language Technologies, vol. 1 (Long Papers), pp. 641–651 (2018)
Yih, W., et al.: semantic parsing via staged query graph generation: question answering with knowledge base. In: ACL, pp. 1321–1331 (2015)
Zou, L., et al.: gStore: a graph-based SPARQL query engine. VLDB J. 23(4), 565–590 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zan, D. et al. (2022). S\(^2\)QL: Retrieval Augmented Zero-Shot Question Answering over Knowledge Graph. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-05981-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05980-3
Online ISBN: 978-3-031-05981-0
eBook Packages: Computer ScienceComputer Science (R0)