Data fusion plays a major role in assisting decision makers by providing them with an improved situational
awareness so that informed decisions could be made about the events that occur in the field. This involves
combining a multitude of sensor modalities such that the resulting output is better (i.e., more accurate, complete,
dependable etc.) than what it would have been if the data streams (hereinafter referred to as 'feeds') from the
resources are taken individually. However, these feeds lack any context-related information (e.g., detected event,
event classification, relationships to other events, etc.). This hinders the fusion process and may result in creating
an incorrect picture about the situation. Thus, results in false alarms, waste valuable time/resources.
In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have
proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular
event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be
related to the current fusion. We use a subset of Web Ontology Language (OWL), OWL-DL to present a
lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts.
We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We
discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed
could assist analysts, planners to make better informed decisions.