There is a need for a strategic, semantics-oriented framework for scientific document tagging. With scientific document tagging having received limited attention in recent times, and the internet’s evolution toward Web 3.0, it is imperative to establish a framework for recommending tags for scientific documents. This paper proposes a framework for semantics-oriented scientific document tagging in which the dataset of documents is subjected to TF-IDF and categorical extraction which is in-turn used to synthesize domain-specific ontologies for generating the initial auxiliary knowledge will is strengthened by generation of the metadata and classified by the Deep Belief Networks, which is a robust deep learning classifier. The dataset of documents is also classified using DBNs and enriched with Wikidata for entity enrichment. The framework employs Twitter Semantic Similarity and KL Divergence with distinct thresholds and step deviance measures to achieve semantics-oriented reasoning. The Tanimoto similarity over Lightning Search Algorithm helps in intermediate refinement of initial feasible solution sets into much more optimal solution sets. The proposed framework demonstrates exceptional performance, achieving an overall precision rate of 94.09%, with a false discovery rate (FDR) of 0.06 and an impressive F-measure of 95.56%. This positions our framework as a state-of-the-art model for scientific document tagging, surpassing other baseline models in terms of performance.