Abstract
Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the case of the SNA analysis, the validity of the results obtained with the knowledge engineering approach are compared to those obtained manually using the UCINET, Cytoscape, Pajek and Gephi to test the accuracy of the knowledge model.
Abstract (translated)
酒精使用障碍（AUD）是全球公共卫生组织关注的主要问题，尤其是在青少年群体方面。青少年饮酒的行为已知受到看到朋友饮酒，甚至父母饮酒的影响。在这个基础上，许多研究在青少年中探讨饮酒行为时，使用了社交网络分析（SNA）技术来研究青少年所涉及的不同的社交网络（朋友，同学，家人等）。这类研究需要通过问卷调查进行初步的数据收集，然后使用SNA技术进行后续分析。这一过程涉及多个手动数据处理阶段，这些阶段耗时且容易出错。利用知识工程技术（包括构建领域本体论）来表示信息，使所有活动（从数据收集到SNA研究的结果）实现自动化。本文展示了如何构建一个知识模型，并将其与传统方法进行比较，详细说明了后者的主要优势。在SNA分析方面，将知识工程方法获得的分析结果与使用UCINET、Cytoscape、Pajek和Gephi手动获得的分析结果进行了比较，以测试知识模型的准确性。
URL
https://arxiv.org/abs/2402.10967
