Title: The self-increasing expansion method for knowledge space based on deep learning algorithm

Authors: Yuanhan Weng; Jingan Wang

Addresses: School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211000, China ' Zhongshan College, University of Electronic Science and Technology of China, Zhongshan, 528402, China

Abstract: In order to overcome the problems of the traditional expansion method for knowledge space, such as small expansion range and low accuracy, this paper proposes an expansion method for knowledge space based on deep learning algorithm. Through deep learning algorithm, combined with multi-modal information fusion method, including the fusion and expansion of the current knowledge space, the knowledge space expansion framework is constructed. The framework is set as space organisation knowledge, knowledge indexing, knowledge navigation, knowledge retrieval and other parts, and knowledge division is realised according to the continuous classification of knowledge sequence information. In the extended space, the multi-structure state of knowledge element is integrated by semantic description technology to realise the expansion of knowledge space. Experimental results show that the expansion method for knowledge space based on deep learning algorithm is better.

Keywords: deep learning; multi-modal information fusion; knowledge expansion; semantic description; knowledge of yuan.

DOI: 10.1504/IJICT.2022.119318

International Journal of Information and Communication Technology, 2022 Vol.20 No.1, pp.65 - 82

Received: 12 May 2020
Accepted: 22 Jun 2020
Published online: 01 Dec 2021 *

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