This paper proposes a strategic ontology knowledge integration framework for Web 3.0 through ontology-based prompting since Web 3.0 is shifting into a more organized era. This paper proposes a strategic ontology knowledge integration for political science and sports journalism as the domains of choice. Which incrementally increases the Auxiliary knowledge encompassment through topic modelling, metadata generation, and integrating entities through standard knowledge store repositories like the Google KG API. The model also formalizes ontologies from the categorical terms and from the classified instances of metadata which is achieved using the GANs classifier. Ontology shuffling is used to generate prompts from the bigram and trigram applied, pre-processed query words from the user query to increase the lateral assembly of the entities and thereby increase the overall diversity of querying in the model. The model strategically computes semantic similarities between the entities at different stages in the pipeline through Bell’s isolation index, Jaccard’s similarity, Jiang Conrath similarity Measure, and also Twitter Semantic Similarity. Reyni’s Entropy helps in the synthesis of ontology, thereby organizing the knowledge accumulated in the model. Classification of the dataset through GANS and encompassment of semantic similarity measures to rank the documents helps in knowledge integration into the ontology which is optimizes using the Intelligent Water Drop Algorithm. This model can recommend documents as well as synthesize knowledge for political and sports journalism. This framework achieves a precision of 97.22%, an accuracy of 97.96%, and an FDR of 0.03