The maritime security domain is challenged by a number of data analysis needs focusing on increasing the maritime situation awareness, i.e., detection and analysis of abnormal vessel behaviors and suspicious vessel movements. The need for efficient processing of dynamic and/or static vessel data that come from different heterogeneous sources is emerged. In this paper we describe how we address the challenge of combining and processing real-time and static data from different sources using ontology-based data access techniques, and we explain how the application of semantic web technologies increases the value of data and improves the processing workflow in the maritime domain.