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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.
Fuzzy Formal Concept Lattice, Machine Learning, Ontology, Semantic Web, Web Text Analysis.
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