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.