Abstract
Knowledge Base Question Answering (KBQA) is a popular research direction in knowledge graphs, and most of KBQA work focuses on natural language parsing. In order to enhance the application of cross-lingual knowledge, the 16th China Conference on Knowledge Graph and Semantic Computing (CCKS2022) has released a cross-lingual knowledge base question answering (CKBQA) task. This paper presents the submission of our team (HW-TSC) to the CKBQA task, in which we propose a search-enhanced path mining and ranking method. The method divides the process of CKBQA into four parts: question classification, principal entity extraction, search-enhanced candidate path mining and candidate path ranking. Finally, both the preliminary and final evaluation results prove the effectiveness of our method.
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
References
Shadbolt, N., Berners-Lee, T., Hall, W.: The semantic web revisited. IEEE Intell. Syst. 21(3), 96–101 (2006)
Singhal,A.: Introducing the knowledge graph: things, not strings. Offic. Blog Google 5, 16 (2012)
Wang, H., Zhang, F., Zhao, M., et al.: Multi-task feature learning for knowledge graph enhanced recommendation. In: The World Wide Web Conference, pp. 2000–2010 (2019)
Berant, J., Chou, A., Frostig, R., et al.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1533–1544 (2013)
Abujabal, A., Yahya, M., Riedewald, M., et al.: Automated template generation for question answering over knowledge graphs. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1191–1200 (2017)
Bordes, A, Chopra, S, Weston, J.: Question answering with subgraph embeddings. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 615-620 (2014)
Yao, X., Van Durme, B.: Information extraction over structured data: question answering with freebase. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 956–966 (2014)
Kenton, J.D.M.W.C., Toutanova, L.K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186 (2019)
Conneau, A., Khandelwal, K., Goyal, N., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440–8451 (2020)
Miyato, T., Dai, A.M., Goodfellow, I.: Adversarial training methods for semi-supervised text classification. arXiv preprint (2016)
Li, J., Fei, H., Liu, J., et al.: Unified named entity recognition as word-word relation classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 10, pp. 10965–10973 (2022)
Gormley, C., Tong, Z.: Elasticsearch: the Definitive Guide: a Distributed Real-Time Search and Analytics Engine. O’Reilly Media, Inc. (2015)
Lui, M., Baldwin, T.: langid. py: An off-the-shelf language identification tool. In: Proceedings of the ACL 2012 System Demonstrations, pp. 25–30 (2012)
Feng, F., Yang, Y., Cer, D., et al.: Language-agnostic BERT sentence embedding. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 878–891 (2022)
Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with gpus. IEEE Trans. Big Data 7(3), 535–547 (2019)
Kudo, T., Richardson, J.: Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 66–71 (2018)
Tang, Y., Tran, C., Li, X., et al.: Multilingual translation with extensible multilingual pretraining and finetuning. arXiv preprint (2020)
Chi, Z., Dong, L., Wei, F., et al.: InfoXLM: An information-theoretic framework for cross-lingual language model pre-training. In: NAACL-HLT (2021)
Bengio, Y., Louradour, J., Collobert, R., et al.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009)
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 Singapore Pte Ltd.
About this paper
Cite this paper
Wu, Z. et al. (2022). A Search-Enhanced Path Mining and Ranking Method for Cross-lingual Knowledge Base Question Answering. In: Zhang, N., Wang, M., Wu, T., Hu, W., Deng, S. (eds) CCKS 2022 - Evaluation Track. CCKS 2022. Communications in Computer and Information Science, vol 1711. Springer, Singapore. https://doi.org/10.1007/978-981-19-8300-9_4
Download citation
DOI: https://doi.org/10.1007/978-981-19-8300-9_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8299-6
Online ISBN: 978-981-19-8300-9
eBook Packages: Computer ScienceComputer Science (R0)