Abstract
We propose a staged framework for question answering over a large-scale structured knowledge base. Following existing methods based on semantic parsing, our method relies on various components for solving different sub-tasks of the problem. In the first stage, we directly use the result of entity linking to obtain the topic entity in a question, and simplify the process as a semantic matching problem. We train a neural network to match questions and predicate sequences to get a rough set of candidate answer entities from the knowledge base. Unlike traditional methods, we also consider entity type as a constraint on candidate answers to remove wrong candidates from the rough set in the second stage. By applying a convolutional neural network model to match questions and predicate sequences and a type constraint to filter candidate answers, our method achieves an average F1 measure of 74.8% on the WEBQUESTIONSSP dataset, it is competitive with state-of-the-art semantic parsing approaches.
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Notes
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Available at http://aka.ms/WebQSP.
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Acknowledgements
The work is supported by the Natural Science Foundation of China under grant No. 61502095, and the Natural Science Foundation of Jiangsu Province under Grant BK20140643.
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Chen, Y., Li, H., Xu, Z. (2019). Convolutional Neural Network-Based Question Answering Over Knowledge Base with Type Constraint. In: Zhao, J., Harmelen, F., Tang, J., Han, X., Wang, Q., Li, X. (eds) Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding. CCKS 2018. Communications in Computer and Information Science, vol 957. Springer, Singapore. https://doi.org/10.1007/978-981-13-3146-6_3
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