Abstract
Semantic parsing, as an essential approach to question answering over knowledge bases KBQA), transforms a question into query graphs for further generating logical queries. Existing semantic parsing approaches in KBQA mainly focus on relations (called local semantics) with paying less attention to the relationship among relations (called global semantics). In this paper, we present a seq2seq-based semantic parsing approach to improving performance of KBQA by converting the identification problem of question types to the problem of machine translation. Firstly, we introduce a BiLSTM-based named entity recognition (NER) method to extract all classes of entities occurring in questions. Secondly, we present an attention-based seq2seq model to learn one type of a question by applying seq2seq model in extracting relationships among classes. Finally, we generate templates to adopt more question types for matching more complex questions. The experimental results on a real knowledge base Chinese film show that our approach outperforms the existing template matching model.
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Acknowledgments
This work is supported by the National Key Research and Development Program of China (2017YFC0908401) and the National Natural Science Foundation of China (61972455). Xiaowang Zhang is supported by the Peiyang Young Scholars at Tianjin University (2019XRX-0032).
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Wu, L., Wu, P., Zhang, X. (2020). A Seq2seq-Based Approach to Question Answering over Knowledge Bases. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_17
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DOI: https://doi.org/10.1007/978-981-15-3412-6_17
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