Semantic-driven modelling of context and entity of interest profiles for maritime situation awareness

One of the inherent aspects of Situation Awareness (SA) is the correct interpretation of the perceived situational picture, to enable future projection and support decision making [1]. In surveillance, security operators must be able to focus on the most important events in the picture, and to evaluate the threat risk, which is assessed in relation to the event context. The notion of context has long been investigated in pervasive computing, mostly for Internet of Things and com- puter SA. In this position paper, we propose a formalisation of SA context, applica- ble to events interpretation for security and safety threat assessment. We exemplify it for Maritime SA (MSA), to contextualise maritime events, facts and anomalies, and assess the risk associated to maritime threats. The formalisation relies on en- tity profiles, which represent the relevant historical knowledge on the entities of interest for security, i.e., vessels, areas, information sources. Profiles are built over time, updating and elaborating the observations generated by the maritime sensor network, an approach suitable to Linked Data and Knowledge Graphs.