Characterizing Knowledge Graphs using Dirichlet-Multinomial Soft Stochastic Block Modeling
Background Knowledge graphs are widely used to represent various types of entity relationships and several methodologies have been developed to characterize and predict their structure, see also [1,2] for surveys. Typically these characterizations are based on various approaches to characterizing similarities among the entities. Recently, it has been demonstrated that the Dirichlet-ulmtinomial stochastic block model can be used to identify entite structures in terms of how they optimally differentiate in their relational structure across a set of graphs (i.e., across the relationships) [3,4]. This project will advance such modeling approaches to provide a scalable and new framework for the modeling of knowledge graphs in which entities are defined in terms of such optimally differentiating properties. ...