Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis.

中文翻译：

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使用社交网络数据的基于本体的推荐系统

在线社交网络（OSN）被认为是实时决策的关键信息来源。然而，一些限制导致研究人员可以获得的信息量减少，同时增加了社交网络挖掘过程的时间。在此背景下，本文提出了一种新的在线社交网络（OSN）采样框架。领域知识用于定义定制策略，可以减少挖掘所需的预算和时间，同时提高召回率。本体支持我们的过滤层评估节点的相关性。我们的方法表明，可以改进相同的机制来向用户提示推荐。我们的测试用例和实验结果强调了社交挖掘器中策略定义步骤的重要性以及推荐分析领域知识图上本体的应用。