Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms
In the digital era, the accuracy and reliability of information are paramount, and fact-checking is essential to achieving this objective. Researching this area presents numerous challenges, particularly for the Vietnamese language, due to the current scarcity of tools and data for Vietnamese fact-c...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10816605/ |
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| author | Huong To Duong Van Hai Ho Phuc do |
| author_facet | Huong To Duong Van Hai Ho Phuc do |
| author_sort | Huong To Duong |
| collection | DOAJ |
| description | In the digital era, the accuracy and reliability of information are paramount, and fact-checking is essential to achieving this objective. Researching this area presents numerous challenges, particularly for the Vietnamese language, due to the current scarcity of tools and data for Vietnamese fact-checking. To address these challenges and advance fact-checking research both broadly and within the Vietnamese context, this paper introduces a fact-checking model tailored for Vietnamese, named ViKGFC. ViKGFC integrates a Knowledge Graph (KG), inference rules, and the Knowledge graph - Bidirectional Encoder Representations from Transformers (KG-BERT) deep learning model. Its notable capability is the extraction of triples from complex Vietnamese sentences, which significantly enhances information extraction in the Vietnamese language. Additionally, ViKGFC utilizes inference rules to improve the KG’s accuracy and employs matching techniques for rapid verification, thereby aiding in the swift prevention of misinformation spread on media and online platforms. Evaluated on a dataset of 130,190 Vietnamese samples sourced from Wikipedia, ViKGFC attains an outstanding accuracy level, reaching 95%. This proposed method offers an optimistic solution for verifying facts in Vietnamese and could potentially assist in creating fact-checking tools and techniques for other languages. In summary, this study significantly advances data science by introducing a reliable and accurate approach to Vietnamese fact-checking. |
| format | Article |
| id | doaj-art-d3e26f71c13046a6af57aafa888ed076 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d3e26f71c13046a6af57aafa888ed0762025-01-10T00:01:46ZengIEEEIEEE Access2169-35362025-01-01134517453210.1109/ACCESS.2024.352341610816605Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online PlatformsHuong To Duong0https://orcid.org/0000-0002-1626-0531Van Hai Ho1https://orcid.org/0009-0001-8273-8257Phuc do2Faculty of Information Science and Engineering, University of Information Technology-Vietnam National University, Ho Chi Minh City, VietnamFaculty of Information Technology, FPT University, Ho Chi Minh City, VietnamFaculty of Information Systems, University of Information Technology-Vietnam National University, Ho Chi Minh City, VietnamIn the digital era, the accuracy and reliability of information are paramount, and fact-checking is essential to achieving this objective. Researching this area presents numerous challenges, particularly for the Vietnamese language, due to the current scarcity of tools and data for Vietnamese fact-checking. To address these challenges and advance fact-checking research both broadly and within the Vietnamese context, this paper introduces a fact-checking model tailored for Vietnamese, named ViKGFC. ViKGFC integrates a Knowledge Graph (KG), inference rules, and the Knowledge graph - Bidirectional Encoder Representations from Transformers (KG-BERT) deep learning model. Its notable capability is the extraction of triples from complex Vietnamese sentences, which significantly enhances information extraction in the Vietnamese language. Additionally, ViKGFC utilizes inference rules to improve the KG’s accuracy and employs matching techniques for rapid verification, thereby aiding in the swift prevention of misinformation spread on media and online platforms. Evaluated on a dataset of 130,190 Vietnamese samples sourced from Wikipedia, ViKGFC attains an outstanding accuracy level, reaching 95%. This proposed method offers an optimistic solution for verifying facts in Vietnamese and could potentially assist in creating fact-checking tools and techniques for other languages. In summary, this study significantly advances data science by introducing a reliable and accurate approach to Vietnamese fact-checking.https://ieeexplore.ieee.org/document/10816605/Fact checkingknowledge graphinference datalog ruleKG-BERTBERT |
| spellingShingle | Huong To Duong Van Hai Ho Phuc do Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms IEEE Access Fact checking knowledge graph inference datalog rule KG-BERT BERT |
| title | Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms |
| title_full | Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms |
| title_fullStr | Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms |
| title_full_unstemmed | Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms |
| title_short | Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms |
| title_sort | vietnamese sentence fact checking using the incremental knowledge graph deep learning and inference rules on online platforms |
| topic | Fact checking knowledge graph inference datalog rule KG-BERT BERT |
| url | https://ieeexplore.ieee.org/document/10816605/ |
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