FakeKG: A Knowledge Graph of Fake Claims for Improving Automated Fact-Checking (Student Abstract)
Abstract
False 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.
BibTeX
@article{Shahi_2024, title={FakeKG: A Knowledge Graph of Fake Claims for Improving Automated Fact-Checking (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27020}, DOI={10.1609/aaai.v37i13.27020}, abstractNote={False 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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Shahi, Gautam Kishore}, year={2024}, month={Jul.}, pages={16320-16321} }