Finding Association Rules in Linked Data, a Centralization Approach

Linked Data is used in the Web to create typed links between data from different sources. Connecting diffused data by using these links provides new data which could be employed indifferent applications. Association Rules Mining (ARM) is a data mining technique which aims to find interesting patterns and rulesfrom a large set of data. In this paper, the problem of applying association rules mining using Linked Data in centralizationapproach has been addressed -i.e. arranging collected data from different data sources into a single dataset and then apply ARM onthe generated dataset. Firstly, a number of challenges in collectingdata from Linked Data have been presented, followed by applying the ARM using the dataset of connected data sources. Preliminary experiments have been performed on this semantic data showing promising results and proving the efficiency, robust, and useful of the used approach

کلیدواژه ها:

Data Mining ، Association Rules Mining ، Linked Data Mining ، Frequent Itemset Mining ، Linked Data Query