Data Mining and Knowledge Engineering

Brinda S. Pujara ¹, Sahista S. Machchhar ¹

Affiliations
1 Marwadi Education Foundation Group of Institutions, Rajkot-360007, GJ, IN

Abstract

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 achieved with the help of user-specified query and other supporting parameters. While the uniqueness will be achieved by eliminating the rules base on subsumption.

Keywords

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

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Brinda S. Pujara ¹, Sahista S. Machchhar ¹

Affiliations
1 Marwadi Education Foundation Group of Institutions, Rajkot-360007, GJ, IN

Abstract

Current Web includes data in various forms such as text, image, video, audio, etc. Thus data is highly unstructured and heterogeneous. In today's world of fast growing technology there is a great need for the knowledge discovery regarding web data, which was demanded to match the relevance of the data, presented to the user. Thus, it demands for techniques which make the user data machine understandable. Semantic web provides various ways to discover and extract knowledge from web using semantics or we can say metadata, by using various data mining techniques. This paper presents a brief layout of semantic web mining and will provide information about various data mining techniques like Association, Classification, Clustering, etc. for semantic web data, which will be helpful for knowledge discovery. Also, comparison of various data mining techniques is provided.

Keywords

Association Rules, Classification, Clustering, Data Mining, Semantic Web Mining, Machine Learning, Soft Computing.

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