Fuzzy Concept Lattice for Ontology Learning and Concept Classification

Objective: To discover the the new latent concept, which is significant to self-learning and machine learning. To better understand the conceptual relations of the query terms. Methods: The application of Fuzzy Formal Concept to construct a Concept Lattice that better describes the semantic relations of incoming patterns for a collection of documents. There has been little work that evaluates the effect of various techniques and parameter settings in the word space construction from corpora. Findings: The present paper experimentally investigates how the choice of a particular domain helps the user to discover the information which is much closer to his preferences. Applications/Improvements: The results generated using our novel approach has been experimented for the context of finding the Sea side Schools based on user requirements. A comparision of related papers is performed to encounter the challenging issues of Ontology construction for text classification in Semantic Web.