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ARTICLE

A Survey of Knowledge Based Question Answering with Deep Learning

Chaoyu Deng, Guangfu Zeng, Zhiping Cai, Xiaoqiang Xiao*
National University of Defense Technology, Changsha, 410073, China
* Corresponding Author: Xiaoqiang Xiao. Email: .

Journal on Artificial Intelligence 2020, 2(4), 157-166. https://doi.org/10.32604/jai.2020.011541

Received 21 May 2020; Accepted 15 July 2020; Issue published 31 December 2020

Abstract

The purpose of automated question answering is to let the machine understand natural language questions and give accurate answers in the form of natural language. This technology requires the machine to store a large amount of background knowledge. In recent years, the rapid development of knowledge graph has made the knowledge based question answering (KBQA) more and more popular. Traditional styles of KBQA methods mainly include semantic parsing, information extraction and vector modeling. With the development of deep learning, KBQA with deep learning has gradually become the mainstream method. This paper introduces the application of deep learning in KBQA mainly from the following aspects: the development history of KBQA, KBQA methods using deep learning, common datasets used in KBQA, the comparison of various methods and the future trend.

Keywords

Deep learning; question answering; knowledge graph

Cite This Article

C. Deng, G. Zeng, Z. Cai and X. Xiao, "A survey of knowledge based question answering with deep learning," Journal on Artificial Intelligence, vol. 2, no.4, pp. 157–166, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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