Abstract
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.
Abstract (translated)
近年来，社交网络理论和方法的运用已经应用于许多领域，包括公共卫生。进行社交网络分析（SNA）的完整程序是一个耗时且容易出错的过程，在这个过程中，社交网络分析专家可能会犯错误。这项研究提出了一个多领域知识模型，能够自动收集数据并在不同领域进行不同的社交网络分析，不会出现错误，并获得与SNA专家相同的结论。该模型由OntoSNAQA ontology组成，包含类、属性和规则，表示People、Questionnaires和Social Network Analysis领域。除了OntoSNAQA本身之外，不同的规则由SWRL和SPARQL查询表示。使用OntoSNAQA创建了一个知识基础系统，并将其应用于一个实际案例研究，以展示该方法的优势。最后，将SNA分析的结果与一些最广泛使用的SNA应用程序（UCINET、Pajek、Cytoscape和Gephi）进行比较，以测试并证实模型的有效性。
URL
https://arxiv.org/abs/2402.02181
