Concept cognition for knowledge graphs: Mining multi-granularity decision rule

As part of cognitive intelligence, concept cognition for knowledge graphs aims to clearly grasp the typical characteristics of the things referred to by the concept, which can provide prior knowledge for machine understanding and thinking. Different from concept learning and formal concept analysis that learn new concepts from data and the general decision rule that comes from an independent decision table, this paper cognizes an existing concept by decision rules that come from multiple granularities. Specifically, 1) concept cognition for knowledge graphs is realized from the perspective of mining multi-granularity decision rule. 2) Decision tables corresponding to four granularities form a multi-granularity decision table group, and then the result from coarser granularity can guide and help obtaining the result from finer granularity. 3) We propose a framework for mining multi-granularity decision rules, which involves going from a multi-granularity decision table group to the frequent maximal attribute patterns to the decision rules to the credible decision rules. Finally, we verified effectiveness of dividing positive and negative data, monotonicity of attribute patterns in a multi-granularity decision table group, and downward monotonicity of credibility, and observed the impact of the parameter min_cov and min_conf on execution times.