Most of the existing event recommendation frameworks are not Web 3.0 compliant, which brings up the need for an event recommendation framework in the era of Web 3.0. Although it is organized structurally, Web 3.0 is highly cohesive when compared to Web 2.0 and the traditional web algorithms for event recommendation do not confirm to the standards set by Web 3.0 which has high topic and knowledge density. The model accumulates categories from dataset of events subjected to incremental addition of auxiliary knowledge through topic modeling using LDA, Wikidata, generating a dynamic knowledge stack from the World Wide Web through an agent, and classify it using a transformer to make it more permeable into the model. The XGBoost classifier is used to classify the dataset of events. The knowledge graph API by Google provides the amount of auxiliary knowledge that is encompassed into the model. Semantic similarity is handled through Tanimoto similarity measure. Normalized compression distance and Twitter semantic similarities are computed at different stages in the architectural pipeline. The Jaguar algorithm functions as the most effective metaheuristic algorithm for optimization, converting the original set of solutions into a significantly more optimal set. 95.47% of average precision, 97.12 of average recall, 96.30 of average accuracy, 96.29% of average F-measure, and an FDR value of 0.05 has been achieved by the proposed SATE model which out performs all the other baseline models and serves as a leading-edge model for event recommendation using semantics.