基于集体智能的语文学科知识图谱构建

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 : 2023-07-31 DOI:10.4018/ijswis.327355
Guozhu Ding, Peiying Yi, Xinru Feng
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引用次数: 1

摘要

知识图谱是智能辅导系统的一个有价值的工具,通常以客观性和准确性为重点构建。然而,它们可能无法有效地捕捉到人文学科中经常出现的主观性和复杂关系。为了解决这一问题,采用集体智能方法开发了主题知识图的动态可视化,该方法集成了学习者的个体智能,并考虑了认知多样性来构建和发展知识图。该方法构建了722个知识关联,演化出584个三元组。一项调查评估了该方法的有效性和用户友好性,表明该方法是有效的、易于使用的,并且可以改进主题知识本体。综上所述,结合个人和集体智慧是在具有主观性和复杂性的学科领域中构建有效知识图谱的一种很有前途的方法。
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Constructing a Knowledge Graph for the Chinese Subject Based on Collective Intelligence
Knowledge graphs are a valuable tool for intelligent tutoring systems and are typically constructed with a focus on objectivity and accuracy. However, they may not effectively capture the subjectivity and complex relationships often present in the humanities. To address this issue, a dynamic visualization of subject matter knowledge graph was developed using a collective intelligence approach that integrates the individual intelligence of learners and considers cognitive diversity to construct and evolve the knowledge graph. The approach resulted in the construction of 722 knowledge associations and the evolution of 584 triples. A survey assessed the effectiveness and user-friendliness, revealing that this approach is effective, easy to use, and can improve subject matter knowledge ontology. In conclusion, combining individual and collective intelligence is a promising approach for building effective knowledge graphs in subject areas with subjectivity and complexity.
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来源期刊
CiteScore
6.20
自引率
12.50%
发文量
51
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.
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