A Novel Model for Mining Association Rules from Semantic Web Data

The amount of ontologies and semantic annotations for various data of broad applications is constantly growing. This type of complex and heterogeneoussemantic data has created new challenges in the area of data mining research. Association Rule Mining is one of the most common data mining techniques which can be defined as extracting the interesting relation among large amount of transactions. Since this technique is moreconcerned about data representation, we can say it is the most challenging data mining technique to be applied on semantic web data. Moreover, the Semantic Webtechnologies offer solutions to capture and efficiently use the domain knowledge. So, in this paper, we propose a novelmethod to provide a way to address these challenges and enable processing huge volumes of semantic data, perform association rule discovery, store these new semantic rulesusing semantic richness of the concepts that exist in ontology and apply semantic technologies during all phases of mining process.

کلیدواژه ها:

Semantic Annotated Data ، Association Rule Mining ، Ontology