期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2015
卷号：7
页码：106-115
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：Large growing knowledge bases have been an inter- esting field in many researches in recent years. Most techniques focus on building algorithms to help the Knowledge Base (KB) automatically (or semi-automatically) extends. In this article, we make use of an association (or generalized association) rule mining algorithm in order to populate the KB and to increase the relations between KB's categories. Considering that most systems constructing their large knowledge bases continuously grow, they do not contain all facts for each category, resulting in a missing value dataset. To accomplish that, we developed a new parameter, called MSC (Modified Support Calculation) measure. This measure also contributes to generate new and significant rules. Nevertheless, association rules algorithms gen- erates many rules and evaluate each one is a hard step. So, we also developed a structure, based on pruning obvious itemsets and generalized association rules, which decreases the amoun- t of discovered rules. The use of generalized association rules contributes to their reduction. Experiments confirm that our approaches discover relevant rules that helps to populate our knowledge base with instances (by MSC measure and associa- tion rules), increase the relationships between the KB's domains (using generalized association rules) as well as facilitate the pro- cess of evaluating extracted rules (pruning obvious itemset and association rules).
