Research on the Construction and Application of Knowledge Graph in the Ceramic Field Based on Natural Language Processing

IF 4.1 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal on Semantic Web and Information Systems Pub Date : 2023-08-01 DOI:10.4018/ijswis.327352

Yu Nie, Na Huang, Junjie Peng, Guanghua Song, Yilai Zhang, Yongkang Peng, Chenglin Ni

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引用次数: 0

Abstract

There are problems of knowledge deficiency and effective unified expression of knowledge in the process of relevant knowledge data acquired by workers in the ceramic domain. In this study, the authors designed relevant experiments to construct ceramic field knowledge graphs to solve these problems. In the experiments of named entity recognition and relationship recognition, the authors compared the performance of several models in OwnThink and ceramics field datasets. The experimental results showed that the BiLSTM-CRF model is the best for named entity recognition and the TextCNN model is the best for relationship recognition in ceramics field datasets. Therefore, the first used the BiLSTM-CRF model to complete the naming entity recognition and then combined with the TextCNN model to complete the relationship recognition to construct the ceramic field knowledge graph. Then, they applied the constructed graph to the ceramic knowledge Q&A service to provide accurate data retrieval service for ceramic domain workers.

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基于自然语言处理的陶瓷领域知识图谱构建与应用研究

陶瓷领域工作者在获取相关知识数据的过程中存在知识缺乏和知识有效统一表达的问题。在本研究中，作者设计了相关的实验来构建陶瓷领域知识图谱来解决这些问题。在命名实体识别和关系识别的实验中，作者比较了几种模型在OwnThink和陶瓷现场数据集上的性能。实验结果表明，在陶瓷现场数据集中，BiLSTM-CRF模型对命名实体识别效果最好，TextCNN模型对关系识别效果最好。因此，首先使用BiLSTM-CRF模型完成命名实体识别，然后结合TextCNN模型完成关系识别，构建陶瓷领域知识图谱。然后，将构造好的图应用于陶瓷知识问答服务，为陶瓷领域工作者提供准确的数据检索服务。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal on Semantic Web and Information Systems 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.50%

发文量

审稿时长

20 months

期刊介绍： The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.