Exploring Association Rules in a Large Growing Knowledge Base

Abstract

Large growing knowledge bases have been an interesting 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 generates 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 amount 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 association rules), increase the relationships between the KB’s domains (using generalized association rules) as well as facilitate the process of evaluating extracted rules (pruning obvious itemset and association rules).