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.

使用社交网络理论和分析方法应用于不同领域，本研究提出了一个多领域知识模型，能自动收集数据并在不同领域执行社交网络分析，消除了专家在分析过程中可能出现的错误，并获得与专家得出的相同结论。模型用 OntoSNAQA 本体来表示，包括人员、问卷和社交网络分析领域的类、属性和规则，同时利用 SWRL 和 SPARQL 查询表示不同规则。通过应用到一个真实案例研究来展示该方法的优势，并将模型得到的社交网络分析结果与 UCINET、Pajek、Cytoscape 和 Gephi 等常用软件进行比较，验证了该模型的有效性。