A Complex Network Model for Knowledge Graphs’ Relationships

When dealing with the structure, content, and quality of knowledge graphs (KGs), most analyses focus on entities, overlooking the significance of relationships and their evolution. In this article, we introduce K nowledge REL ationship M odel (KRELM), a novel and efficient graph model that mimics the behavior of facts accumulation in crowdsourced KGs and accurately simulates the evolution of their structure. By modeling the decentralized process of crowdsourcing, KRELM reproduces key distribution patterns found in relationships, demonstrating that the facts in a KG can be generated incrementally, either by adding new entities or by further describing existing ones. Our theoretical analysis of KRELM reveals that the distribution of facts for relationships follows an exponential law for subjects and a power law for objects, enabling a deeper understanding of knowledge graph dynamics. Experimental validation on major KGs shows that KRELM successfully captures a large part of the structure of real-world relationships, and a longitudinal study of Wikidata confirms its effectiveness in predicting relationship evolution. This work opens new avenues for analyzing and benchmarking KGs.