Classification of Semantic Documents Based on WordNet

There are a lot benefits to enable intelligent agent understanding the information from semantic web. It enhances the efficiency of information usage and at the same time, suffices the need of users. Semantic documents contain adequate semantic information which helps understanding. However, discrepancy between ontology which is an interpreter of semantic document prevents the share of knowledge. In this paper, we proposed a uniform representation for the content, which include concepts and relations, of semantic documents based on WordNet. First, disambiguation is preceded within the key words in a document for the purpose of mapping them to concepts. Then we present the whole document in the form of concept graph that Levenshtein Distance could be applied for making a classification of documents. We have empirical result that this methodology makes a promising raise in accuracy.