Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms

Abstract

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.

Keywords

• Fact checking
• knowledge graph
• inference datalog rule
• KG-BERT
• BERT