The increased usage of information technologies in educational tasks resulted in high volume of data, exploited to build analytical systems that can provide practical insight in the learning process. In this paper, we propose a method of running social network analysis on multiple data sources (academic years, communication tools). To achieve this, the collected data that describe social interactions were converted into a common format by employing a prior developed semantic web educational

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... logy. Using a mapping language the relational data set was linked to the appropriate concepts defined in the ontology and then it was exported in RDF format. The means for SPARQL access was also provided. Subsequently, query patterns were defined for different social interactions in the educational platform. To prove the feasibility of this approach, Gephi tool set was used to run SNA (Social Network Analysis) on data obtained with the SPARQL queries. The added value of this research lies in the potential of this method to simplify running social network analysis on multiple data sets, on a specific course or the entire academic year, by simply modifying the query pattern.