Applying weighted property approaches to extremely large or dynamic RDF datasets poses several challenges and limitations. One major challenge is the computational complexity and resource requirements associated with processing and analyzing large volumes of data. As the size of the dataset increases, the time and resources needed to calculate and update weights for each property also escalate. To address this, techniques such as parallel processing, distributed computing, and optimized algorithms can be employed to enhance scalability and efficiency. Additionally, implementing incremental learning approaches that update weights incrementally as new data is added can help manage dynamic datasets more effectively. Another challenge is the potential for overfitting or bias in the weighting process, especially with large and diverse datasets. Regular validation and testing procedures, as well as incorporating regularization techniques, can help mitigate these issues and ensure the robustness and generalizability of the weighted property approach.