Title: Construction method of knowledge graph under machine learning

Authors: Peifu Han; Junjun Guo; Hua Lai; Qianli Song

Addresses: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, Yunnan, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, Yunnan, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, Yunnan, China ' Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, Yunnan, China

Abstract: With the increasing trade among China and Southeast Asian countries, cultural exchanges have become more and more intensified. Convenient language communication constitutes an important part of the cooperation channels among different countries. To explore the named entity recognition (NER) in the field of knowledge graph construction, the Vietnamese grammar and word formation are analysed deeply in this study, aiming to solve the low recognition precision and low network calculation efficiency in Vietnamese named entity recognition. Firstly, the Vietnamese person names, location names, and institution names in Vietnamese corpus are collected statistically to build a corresponding entity database to assist the Vietnamese named entity recognition. Then, a Vietnamese named entity recognition model is proposed based on residual dense block (RDB) convolutional neural network (CNN).

Keywords: Vietnamese; named entity recognition; residual network; knowledge graph.

DOI: 10.1504/IJGUC.2022.10045597

International Journal of Grid and Utility Computing, 2022 Vol.13 No.1, pp.11 - 20

Received: 17 Feb 2021
Accepted: 29 Jun 2021

Published online: 11 Mar 2022 *

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