SALBIS Research Group, Facultad de Ciencias de la Salud, Universidad de León, Campus de Ponferrada Avda/Astorga s/n, C.P. 24402 Ponferrada (León), Spain.
Centro de Tecnología Biomédica/Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain.
Int J Environ Res Public Health. 2018 Oct 31;15(11):2420. doi: 10.3390/ijerph15112420.
Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network-generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error-prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called "ontology" that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.
社会网络分析（SNA）是一套在社会和行为科学研究领域发展起来的技术，旨在描述和研究一组个体之间建立的社会关系。当为执行 SNA 分析构建社会网络时，首先要完成一个数据收集过程，以提取个体及其关系的特征。这通常是通过完成一份包含不同类型问题的问卷来实现的，这些问题将用于获得执行研究所需的 SNA 度量。因此，有大量不同的可能的网络生成问题，也有许多将响应映射到相应特征和关系的可能性。这些问题可能会引入许多变化（问题的提出方式、每个响应的权重等），这可能会对生成的网络产生影响。所有这些不同的变化都很难手动实现，因为这个过程既耗时又容易出错。本文中描述的工具使用语义知识表示技术来促进这种敏感性研究。该工具的基础是一个称为“本体”的概念结构，它能够表示不同的概念及其定义。该工具与其他类似的工具进行了比较，并强调了该方法的优势，从正在进行的关于青少年饮酒习惯的 SNA 研究中给出了一些具体示例。
