Inference Algorithm for Knowledge Bases with Rule Cluster Structure

This paper presents an inference algorithm for knowledge bases with a rule cluster structure. The research includes the study of the efficiency of inference, measured by the number of cases in which the inference was successful. Finding a rule whose premises are true and activating it leads to extracting new knowledge and adding it as a fact to the knowledge base. We aim to check which clustering and inference parameters influence the inference efficiency. We used four various real datasets in our experimental stage. Overall, we proceeded with almost twenty thousand experiments. The results prove that the clustering algorithm, the amount of input data, the method of cluster representation, and the subject of clustering significantly impact the inference efficiency.