Ontology Based Concept Hierarchy Extraction of Web Data

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This paper proposes the method of Ontology Based Concept Hierarchy Extraction of Web Data. This helps to extract Concept Hierarchy efficient way for ontology construction. It is very useful for learning the ontology from the text in more efficient way. In General, Natural Language is Complexity and Uncertainty. The existing system used either Statistical based learning or logic based learning Techniques. Statistical based learning techniques gives solution only for complexity and Logic based techniques gives solution for uncertainty alone. But the Statistical Relational Learning Techniques give solution for both Complexity and Uncertainty. So, our proposed system uses Statistical Relational Learning Technique, named Markov Logic Network. Markov Logic Network is a technique in which identify the concept in the domain and order the candidate terms in hierarchical way. An experimental result provides the best concept hierarchy extractions compared to the state-of-art methods. Keywords: Concept Hierarchy Extraction, Hearst Pattern, Markov Logic Network, Ontology, Semantic Web.