Hierarchical Fuzzy Signatures are generalizations of the Vector Valued Fuzzy Set concept introduced in the 1970s. A crucial question in the Fuzzy Signature context is what kinds of aggregations are applicable for combining data with partly different substructures. Our earlier work introduced the Weighted Relevance Aggregation method to enhance the accuracy of the final results of calculations based on Hierarchical Fuzzy Signature Structures. In this paper, we further generalise the weights and the aggregation into a new operator called Weighted Relevance Aggregation Operator (WRAO). WRAO enhances the adaptability of the fuzzy signature model to different applications and simplifies the learning of fuzzy signature models from data. We also show the methodology of learning these aggregation operators from data.