KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking

• URL: http://arxiv.org/abs/2601.19447v1
• Date: Tue, 27 Jan 2026 10:32:42 GMT
• Title: KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking
• Authors: Vítor N. Lourenço, Aline Paes, Tillman Weyde, Audrey Depeige, Mohnish Dubey,
• Abstract summary: KG-CRAFT is a method that improves automatic claim verification by leveraging large language models (LLMs)<n>It first constructs a knowledge graph from claims and associated reports, then formulates contextually relevant contrastive questions based on the knowledge graph structure.<n>Extensive evaluations on two real-world datasets demonstrate that our method achieves a new state-of-the-art in predictive performance.
• Score: 3.7841869476488044
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Claim verification is a core component of automated fact-checking systems, aimed at determining the truthfulness of a statement by assessing it against reliable evidence sources such as documents or knowledge bases. This work presents KG-CRAFT, a method that improves automatic claim verification by leveraging large language models (LLMs) augmented with contrastive questions grounded in a knowledge graph. KG-CRAFT first constructs a knowledge graph from claims and associated reports, then formulates contextually relevant contrastive questions based on the knowledge graph structure. These questions guide the distillation of evidence-based reports, which are synthesised into a concise summary that is used for veracity assessment by LLMs. Extensive evaluations on two real-world datasets (LIAR-RAW and RAWFC) demonstrate that our method achieves a new state-of-the-art in predictive performance. Comprehensive analyses validate in detail the effectiveness of our knowledge graph-based contrastive reasoning approach in improving LLMs' fact-checking capabilities.

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