Evidence map›Paper›PMID 38802431›Full record

ArticleScientific reports2024

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

Jianbo Zhao, Huailiang Liu, Weili Zhang, Tong Sun, Qiuyi Chen, Yuehai Wang, Jiale Cheng, Yan Zhuang, Xiaojin Zhang, Shanzhuang Zhang and 2 more

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Jianbo ZhaoSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China. zhaojianbo@stu.xidian.edu.cn.
Huailiang LiuSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Weili ZhangSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Tong SunSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Qiuyi ChenSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Yuehai WangSchool of Artificial Intelligence, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Jiale ChengSchool of Artificial Intelligence, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Yan ZhuangSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Xiaojin ZhangSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Shanzhuang ZhangSchool of Economics and Management, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Bowei LiSchool of Telecommunications Engineering, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.
Ruiyu DingSchool of Artificial Intelligence, Xidian University, 266 Xifeng Road, Xi'an, 710071, China.

Funding

Xi'an Municipal Bureau of Science and Technology 20KYPT0003-10
6 · The paper itself

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.

Indexed as

Domain ontologyNew concept discoveryRumor content featuresSWRL rulesTFITop-level ontology reuse

Identifiers

PMID38802431
PMCPMC11637064

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LicenceCC BY
Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.