  The use of social network theory and methods of analysis have been applied to
different domains in recent years, including public health. The complete
procedure for carrying out a social network analysis (SNA) is a time-consuming
task that entails a series of steps in which the expert in social network
analysis could make mistakes. This research presents a multi-domain knowledge
model capable of automatically gathering data and carrying out different social
network analyses in different domains, without errors and obtaining the same
conclusions that an expert in SNA would obtain. The model is represented in an
ontology called OntoSNAQA, which is made up of classes, properties and rules
representing the domains of People, Questionnaires and Social Network Analysis.
Besides the ontology itself, different rules are represented by SWRL and SPARQL
queries. A Knowledge Based System was created using OntoSNAQA and applied to a
real case study in order to show the advantages of the approach. Finally, the
results of an SNA analysis obtained through the model were compared to those
obtained from some of the most widely used SNA applications: UCINET, Pajek,
Cytoscape and Gephi, to test and confirm the validity of the model.
