Title: Research on self-incremental expansion method of knowledge base based on deep learning

Authors: Qiang Yu; Lanlan Liu

Addresses: School of Geography and Tourism, Harbin University, Harbin 150086, China ' School of Geography and Tourism, Harbin University, Harbin 150086, China

Abstract: In order to overcome the problems of low recall, low precision and long time consuming in current knowledge-base expansion algorithms, a new knowledge-base self-increment expansion algorithm based on deep learning is proposed. The data in the knowledge base is preprocessed by concept stratification, and the running example diagram of the knowledge base is designed according to concept stratification theory. Deep learning tool is used to expand the initial query words of knowledge base, and Word2vec is used to train the document set to build the knowledge base by calculating the cosine similarity. Based on the knowledge base, the noise detection model is constructed by convolution neural network. Through the deep learning, the extended words are filtered to realise the self-expanding of knowledge base. Experimental results show that the proposed algorithm has high recall rate, precision rate, and the algorithm takes less time, which verifies the effectiveness of the proposed algorithm.

Keywords: deep learning; knowledge base; self-incremental expansion; cosine similarity.

DOI: 10.1504/IJICT.2021.116551

International Journal of Information and Communication Technology, 2021 Vol.19 No.1, pp.16 - 30

Received: 18 Dec 2019
Accepted: 20 Feb 2020
Published online: 28 Jul 2021 *

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