We propose a semantic-based methodology for Social Network Analysis (SNA). This methodology addresses computations needed for SNA in a declarative way—in contrast to traditional SNA where computations are procedural. Our ingredients are semantic technologies: We define an ontology to represent graphs, their components (nodes, edges or paths), and the structural relationships between these components. We exploit reasoning capabilities of ontologies to infer structural relations between graph components. We also use ontological queries to perform computations needed in SNA. To demonstrate how does this approach work, we present three showcases of typical network analysis: basic metrics, triadic census, and betweenness centrality. The proposed approaches offer several computational opportunities for analyzing networks with respect to calculation of path-dependent centrality metrics, e.g. in distributed setups.     «

We propose a semantic-based methodology for Social Network Analysis (SNA). This methodology addresses computations needed for SNA in a declarative way—in contrast to traditional SNA where computations are procedural. Our ingredients are semantic technologies: We define an ontology to represent graphs, their components (nodes, edges or paths), and the structural relationships between these components. We exploit reasoning capabilities of ontologies to infer structural relations between graph comp...     »