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基于网络谣言内容特征的领域本体构建研究

Research on domain ontology construction based on the content features of online rumors.

作者信息

Zhao Jianbo, Liu Huailiang, Zhang Weili, Sun Tong, Chen Qiuyi, Wang Yuehai, Cheng Jiale, Zhuang Yan, Zhang Xiaojin, Zhang Shanzhuang, Li Bowei, Ding Ruiyu

机构信息

School of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.

School of Artificial Intelligence, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.

出版信息

Sci Rep. 2024 May 27;14(1):12134. doi: 10.1038/s41598-024-62459-4.

Abstract

Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

摘要

网络谣言传播广泛且难以识别,给社会和个人带来严重危害。为有效检测和治理网络谣言,有必要进行深入的语义分析并了解谣言的内容特征。本文提出一种TFI领域本体构建方法,旨在实现谣言文本内容的语义解析与推理。本文从术语层、框架层和实例层入手,基于顶层本体的重用、核心文献内容特征的提取以及真实语料库中新概念的发现,获取谣言领域本体的核心类(五个父类和88个子类)并定义其概念层次结构。设计对象属性和数据属性以描述实体之间的关系或其特征,并根据真实谣言数据集创建实例层。使用OWL语言对本体进行编码,使用Protégé对其进行可视化,使用SWRL规则和Pellet推理机挖掘和验证本体的隐含知识,并判断谣言文本的类别。本文构建了一个具有高一致性和可靠性的谣言领域本体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb02/11637064/8aeaf860bea8/41598_2024_62459_Fig1_HTML.jpg

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