A knowledge graph is a form of knowledge representation that provides a mechanism for describing the interrelatedness of entities in a dataset. Large knowledge graphs have become increasingly important in AI due to their ability to formalize and classify knowledge, enabling more effective extraction, retrieval, and analysis. They are used extensively in systems such as Google’s Gemini and Bard, and IBM’s Watson platform, to support smarter, context-aware applications in search, recommendation, and decision-making. As AI becomes part of daily life, the ethical implications of how these systems understand, recommend, and decide are significant—making the reliability of their underlying data critical. A key consideration for any dataset is its adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) principles, which aim to ensure the provenance, persistence, and reusability of data. In this context, FAIRness has become a crucial measure for establishing the suitability of knowledge graphs not only for data reliability but also for model reliability in machine learning. This thesis evaluates the three currently available automated tools for assessing knowledge graph FAIRness—F-UJI, FAIR Evaluator, and FAIR Checker—to determine their capabilities and consistency. These tools, while gaining adoption in academic and industrial settings, have not previously been systematically compared. This work applies statistical analysis to assess the consistency of their outputs and finds that, while each tool has strengths, none alone offers a complete view. It proposes a novel consistency measurement to support complementary use of all three tools. The systematic evaluation of FAIRness assessment tools, along with the introduction of a new supporting metric, contributes to more trustworthy knowledge graph assessments. This, in turn, provides a foundation for practitioners and researchers working in dataset curation and machine learning model development, where ethical and technical robustness are increasingly essential.