Hierarchical quotient space-based concept cognition for knowledge graphs

Jiangli Duan, Guoyin Wang, Xin Hu, Huanan Bao

Abstract: As part of cognitive intelligence, concept cognition aims to view a concept from a comprehensive perspective and clearly grasp the concept intension (i.e., the typical attributes of the things referred to by the concept). Since knowledge graphs contain many entities that are beneficial for concept cognition, concept cognition for knowledge graphs is a promising research branch of concept cognition. Unlike general concept cognition, which selects all the attributes belonging to the concept and simultaneously eliminates other attributes, concept cognition for knowledge graphs focuses on determining the significance of concept-relevant attributes. Therefore, we propose a method of processing concept cognition for knowledge graphs that utilizes the hierarchical quotient space to determine attribute significance. Specifically, 1) this is the first study to propose and address concept cognition for knowledge graphs. 2) We propose a method of building a hierarchical quotient space for a concept in a knowledge graph that fully considers the characteristics of the concept-relevant attributes in the knowledge graph. 3) We propose using the distance between two hierarchical quotient spaces with and without a certain attribute to determine the significance of the concept-relevant attributes. Finally, the performance of our solution is measured from multiple aspects.