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.

酒精使用障碍（AUD）是公共卫生组织尤其关注的主要问题，特别是涉及青少年群体。本文介绍了如何构建知识模型，并比较了传统方法与完全自动化模型的结果，详细说明了后者的主要优势。在SNA分析的情况下，将使用知识工程方法获得的结果与使用UCINET、Cytoscape、Pajek和Gephi手动获得的结果进行比较，以测试知识模型的准确性。

基于语义技术的社交网络分析用于青少年酒精依赖个性化描述和风险评估