Assigning weights to properties in RDF graphs can be automated or made more objective through the use of machine learning algorithms and natural language processing techniques. One approach could involve training a model on a large dataset of RDF graphs to learn the importance of different properties based on their impact on the overall similarity measure. This model could analyze the relationships between properties, their frequencies, and their contextual relevance to automatically assign weights. By leveraging advanced algorithms, such as neural networks or clustering techniques, the process can be made more data-driven and less reliant on subjective human judgment. Additionally, incorporating feedback mechanisms to continuously refine and update the weights based on user interactions and feedback can help improve the objectivity of the weighting process.