  Fact-checking is a crucial natural language processing (NLP) task that
verifies the truthfulness of claims by considering reliable evidence.
Traditional methods are often limited by labour-intensive data curation and
rule-based approaches. In this paper, we present FactGenius, a novel method
that enhances fact-checking by combining zero-shot prompting of large language
models (LLMs) with fuzzy text matching on knowledge graphs (KGs). Leveraging
DBpedia, a structured linked data dataset derived from Wikipedia, FactGenius
refines LLM-generated connections using similarity measures to ensure accuracy.
The evaluation of FactGenius on the FactKG, a benchmark dataset for fact
verification, demonstrates that it significantly outperforms existing
baselines, particularly when fine-tuning RoBERTa as a classifier. The two-stage
approach of filtering and validating connections proves crucial, achieving
superior performance across various reasoning types and establishing FactGenius
as a promising tool for robust fact-checking. The code and materials are
available at https://github.com/SushantGautam/FactGenius.
