Concept cognition over knowledge graphs: A perspective from mining multi-granularity attribute characteristics of concepts

Abstract

Humans can better understand and answer questions than machines because they know the cognitive knowledge related to the concept in questions. To equip machines with the cognitive knowledge required for cognizing concepts, concept cognition over knowledge graphs in this study involves mining the cognitive knowledge required by machines, i.e., multi-granularity attribute characteristics of concepts, which enables machines to distinguish or cognize concepts from multiple granularities. First, an algorithm is proposed to mine multi-granularity attributes characteristics of concepts from concept-related knowledge in a knowledge graph, i.e., frequent attributes and attribute values of concepts from multiple granularities. Second, the monotonicity of the multi-granularity attribute pattern is proposed to promote synergy among granularities and accelerate the mining process because the result from coarser granularity can serve as a candidate for the result from finer granularity. Third, the representativeness of the maximal frequent attribute pattern is used to unleash the value of above monotonicity and accelerate the mining process, which enables the algorithm to mine maximal frequent attribute patterns with fewer quantities to derive all frequent attribute patterns in large numbers. Finally, the experiments show that the above algorithm is more efficient than baseline algorithms, the monotonicity of the multi-granularity attribute patterns can accelerate the mining process, the representativeness of the maximal frequent attribute patterns means that the percentage is always less than 5%, the percentages of correctly classified instances by the multi-granularity attribute characteristics are always higher than 90%, and the above classification performance performs better than existing machine learning algorithms at most cases.