FakeKG: A Knowledge Graph of Fake Claims for Improving Automated Fact-Checking (Student Abstract)
Keywords:Knowledge Graph, Fake News, Fake Ontology, Claims, Fact-checking, News Media
AbstractFalse information could be dangerous if the claim is not debunked timely. Fact-checking organisations get a high volume of claims on different topics with immense velocity. The efficiency of the fact-checkers decreases due to 3V problems volume, velocity and variety. Especially during crises or elections, fact-checkers cannot handle user requests to verify the claim. Until now, no real-time curable centralised corpus of fact-checked articles is available. Also, the same claim is fact-checked by multiple fact-checking organisations with or without judgement. To fill this gap, we introduce FakeKG: A Knowledge Graph-Based approach for improving Automated Fact-checking. FakeKG is a centralised knowledge graph containing fact-checked articles from different sources that can be queried using the SPARQL endpoint. The proposed FakeKG can prescreen claim requests and filter them if the claim is already fact-checked and provide a judgement to the claim. It will also categorise the claim's domain so that the fact-checker can prioritise checking the incoming claims into different groups like health and election. This study proposes an approach for creating FakeKG and its future application for mitigating misinformation.
How to Cite
Shahi, G. K. (2023). FakeKG: A Knowledge Graph of Fake Claims for Improving Automated Fact-Checking (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16320-16321. https://doi.org/10.1609/aaai.v37i13.27020
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