Analyzing the use of obvious and generalized association rules in a large knowledge base

Abstract

In recent years, many researches have been focusing their studies in large growing knowledge bases. Most techniques focus on building algorithms to help the Knowledge Base (KB) automatically (or semi-automatically) extends. In this article, we make use of a generalized association rule mining algorithm in order, specially, to increase the relations between KB's categories. Although, 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 rules, which decreases the amount of discovered rules. The use of generalized association rules contributes to their reduction. Experiments confirm that our approach helps to increase the relationships between the KB's domains as well as facilitate the process of evaluating extracted rules.