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A Novel Approach for Extracting Optimized Association Rules using Knowledge Discovery Algorithm from Semantic Web Data

Brinda S. Pujara, Sahista S. Machchhar


The gradual increase in amount of ontology has given rise to heterogeneous and graph structured data, which is a complex data. This complex data of ontology is mainly represented in the form of RDF or OWL. The ontology can be used to extract useful knowledge about the user or the system using that particular ontology, user’s navigation, personalization, association, decision making, medical diagnosis, etc. The basic aim here,  would be to find out efficient methods for improving probably user personalization, decision making  and find out the association that is the relation between various entities of a particular domain, with semantic web mining, association rule mining, and other data mining techniques. With the help of KD-SWD algorithm, unique as well as relevant association rules from the semantic web data can be obtained. Relevancy of the algorithm can be achived with the help of user-specified query and other supporting parameters. While the uniqueness will be achieved by eliminating the rules base on subsumption.


Semantic Web, Association Rules, Unique and Relevant Rules, Ontology, User Personalization and Decision Making.

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