Mining Rule-based Knowledge Bases Inspired by Rough Set Theory

Rule-based knowledge bases are constantly increasing in volume, thus the knowledge stored as a set of rules is getting progressively more complex and when rules are not organized into any structure, the system is inefficient. The aim of this paper is to improve the performance of mining knowledge bases by modification of both their structure and inference algorithms, which in author's opinion, lead to improve the efficiency of the inference process. The good performance of this approach is shown through an extensive experimental study carried out on a collection of real knowledge bases. Experiments prove that rules partition enables reducing significantly the percentage of the knowledge base analysed during the inference process. It was also proved that the form of the group's representative plays an important role in the efficiency of the inference process.