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 heterogeneous semantic 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 more concerned about data representation, we can say it is the most challenging data mining technique to be applied on semantic web data. Moreover, the Semantic Web technologies offer solutions to capture and efficiently use the domain knowledge. So, in this paper, we propose a novel method to provide a way to address these challenges and enable processing huge volumes of semantic data, perform association rule discovery, store these new semantic rules using semantic richness of the concepts that exist in ontology and apply semantic technologies during all phases of mining process.