In this study, the problem of mining association rules in databases with many tables of fuzzy data with taxonomy and tables created using entity-relationship (ER) models is discussed. The majority of the data mining methods in use today deal with datasets that just contain one table. For several tables with ambiguous data and taxonomic structures, few techniques are effective. The "Fuzzy Generalized Rules for ER Models" algorithm is proposed in this paper. In order to find a new algorithm, the study intends to combine the previously published methods Extended Apriori and Apriori star. The research will aid in standardizing algorithms for extracting relevant information from database tables containing information with ambiguous taxonomic structures.