Temporal data modelling in Knowledge Graphs (KGs) refers to the practice of modelling time-varying information in a structured manner. Representing temporal data, enhances KGs’ capabilities by providing comprehensive time-dependent information, making it important for knowledge representation and analysis in various real-world applications. Organisations can enhance data management, user experience, and decision-making processes in their KG applications by selecting the most suitable temporal data modelling approach. Many approaches have been proposed to model temporal data in KGs but they have not been fully evaluated with different metrics. Evaluating different temporal data modelling approaches in KGs using a metrics framework assists objective comparison, informed decision-making, and the advancement of the field. This paper proposes TEDME-KG Metrics Framework, a Metrics Framework for TEmporal Data Modelling Evaluation in Knowledge Graphs. The framework was developed through a comprehensive literature review and the application of the Goal Question Metrics (GQM) method during the design and evaluation of KGs for selected temporal data modelling approaches.