Open Access
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
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. https://doi.org/10.32604/jai.2020.011541
Citations