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.

近年来，社会网络理论和分析方法的使用已应用于包括公共卫生在内的不同领域。进行社交网络分析（SNA）的完整过程是一项耗时的任务，其中包含一系列步骤，其中社交网络分析专家可能会犯错误。这项研究提出了一种多领域知识模型，该模型能够自动收集数据并在不同领域中执行不同的社交网络分析，而不会出错，并获得SNA专家将获得的相同结论。该模型以称为OntoSNAQA的本体表示，该本体由代表人员，问卷和社交网络分析领域的类，属性和规则组成。除了本体本身，SWRL和SPARQL查询表示不同的规则。使用OntoSNAQA创建了一个基于知识的系统，并将其应用于实际案例研究中，以展示该方法的优势。最后，将通过模型获得的SNA分析结果与从一些使用最广泛的SNA应用程序（UCINET，Pajek，Cytoscape和Gephi）获得的结果进行比较，以测试并确认模型的有效性。