The rise of digital platforms has facilitated the rapid spread of disinformation, which poses significant
social, political, and economic challenges. Knowledge graphs (KGs) are emerging as effective tools for
enhancing the accuracy, interpretability, and scalability of fake news detection systems, addressing
limitations in traditional machine learning-based approaches that rely primarily on linguistic analysis. This
work contains a literature review that synthesizes findings from recent studies on the application of KGs
in disinformation detection. We identify how KGs improve detection by encoding real relationships,
analyzing context, and enhancing model interpretability, while also discussing current limitations in
scalability, data completeness, and contextual adaptability. The reviewed studies underscore the need for
future research focusing on scalable, real-time, and cross-linguistic KG models to bolster disinformation
detection capabilities globally. Moreover, we present preliminary results of two use cases, showcasing a
methodology for constructing KGs that can serve as useful tools to fight against disinformation spread.
