Existing frameworks for web-based storyboarding are either based on summarization or sentence parsing and extraction while the synthesis of new knowledge is rare and the addition of existing related entities from datasets is seldom practiced. This paper proposes a web-based storyboarding highly specific to the domain of finance and economics that encompasses concepts of generation of metadata and ontology generation from the terms and categories extracted from the combined dataset. Following this, the metadata’s classification is carried out by a transformer classifier, and the documents themselves are classified using the AdaBoost classifier. KL divergence is used to select features from the intermediate ontologies that are generated. The Adaptive Pointwise Mutual Information (APMI) measure and Simpsons diversity index with differential thresholds and step deviance measures provide a platform for semantics-oriented reasoning through semantic relatedness measures. A metaheuristic optimization model is used in the framework which is the Particle Swarm Optimization which provides an intermediate derivation of the most reliable and relevant entities to populate the optimal solution space. The proposed framework has achieved an average precision of 94.55%, an average F-measure of 95.31%, and an FDR of 0.06.