Chenqi Li
ABSTRACT
In knowledge-based question answering(KBQA), most research adopts the question template matching, which faces with challenges such as unclear entity boundaries and difficult path inference when solving complex questions. In this paper, we propose a KBQA solution based on attribute graph. It extracts the mentions in text to recognize relations and entities, and transforms it into a slot-filling Cypher statement to query the answer. Meanwhile, we design a two-layer network based on a structural attention mechanism to optimize entity boundary identification. The solution provides new ideas of relation recognition for answering complex questions over attribute knowledge graph. Experimental results show that the proposed approach achieves promising performance on both CCKS2019 public dataset and the self-built vertical domain dataset.
- Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26.Google Scholar
- Berant, J., Chou, A., Frostig, R., & Liang, P. (2013, October). Semantic parsing on freebase from question-answer pairs. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1533-1544).Google Scholar
- Yih, S. W. T., Chang, M. W., He, X., & Gao, J. (2015). Semantic parsing via staged query graph generation: Question answering with knowledge base.Google Scholar
- Bordes, A., Usunier, N., Chopra, S., & Weston, J. (2015). Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075.Google Scholar
- Wu, T., Qi, G., Li, C., & Wang, M. (2018). A survey of techniques for constructing Chinese knowledge graphs and their applications. Sustainability, 10(9), 3245.Google Scholar
- Evseev, D. A., & Arkhipov, M. Y. (2020). Sparql query generation for complex question answering with bert and bilstm-based model. In Computational Linguistics and Intellectual Technologies(pp. 270-282).Google ScholarCross Ref
- Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211.Google Scholar
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.Google Scholar
- Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks, 18(5-6), 602-610.Google Scholar
- Saxena, A., Tripathi, A., & Talukdar, P. (2020, July). 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).Google ScholarCross Ref
- Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.Google Scholar
- Yang, Y., He, X., Zhou, K., & Wei, Z. (2019). Multi-Module System for Open Domain Chinese Question Answering over Knowledge Base. arXiv preprint arXiv:1910.12477.Google Scholar
- Ma, J. , Yan, Z. , Pang, S. , Zhang, Y. , & Shen, J. . (2020). Mention Extraction and Linking for SQL Query Generation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).Google ScholarCross Ref
- Ture, F., & Jojic, O. (2016). No Need to Pay Attention: Simple Recurrent Neural Networks Work!(for Answering" Simple" Questions). arXiv preprint arXiv:1606.05029.Google Scholar
- Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.Google Scholar
- Chang, S., Zhang, Y., Han, W., Yu, M., Guo, X., Tan, W., ... & Huang, T. S. (2017). Dilated recurrent neural networks. arXiv preprint arXiv:1710.02224.Google Scholar
- Cui, Y., Che, W., Liu, T., Qin, B., Yang, Z., Wang, S., & Hu, G. (2019). Pre-training with whole word masking for chinese bert. arXiv preprint arXiv:1906.08101.Google Scholar
- Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.Google Scholar
- Lafferty, J., McCallum, A., & Pereira, F. C. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data.Google ScholarDigital Library
Recommendations
Knowledge Graph Embedding Based Question Answering
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data MiningQuestion answering over knowledge graph (QA-KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge in the KG, without ...
Core techniques of question answering systems over knowledge bases: a survey
The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult ...
Quality-aware collaborative question answering: methods and evaluation
WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data MiningCommunity Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this ...
Comments