Abstract Most of the existing web search systems are query-centric and are not user-centric. Mining images from the Web is a challenging task as it requires choosing the right methodology. A strategy that recommends images for homonyms and contextually similar terms have been proposed. The proposed system facilitates ontology modeling for homonyms and contextually related synonymous terms using description logics semantics and semantic similarity computation. An Enhanced Hybrid Semantic Algorithm that computes the semantic similarity and establishes dynamic OntoPath for easing the web image recommendation has been proposed. The proposed system classifies the ontologies using SVM and a Homonym LookUp directory. The methodology focuses on generating unique classes of images as an initial recommendation set. Based on the user click, strategic expansion of OntoPath takes place. Personalization is achieved by content-based analysis of the user click-through data. An overall accuracy of 95.87% is achieved by the proposed system.