Defence R&D Canada is developing a Collaborative Knowledge Exploitation Framework (CKEF) to support the
analysts in efficiently managing and exploiting relevant knowledge assets to achieve maritime domain awareness in joint
operations centres of the Canadian Forces. While developing the CKEF, anomaly detection has been clearly recognized
as an important aspect requiring R&D. An activity has thus been undertaken to implement, within the CKEF, a proof-of-concept
prototype of a rule-based expert system to support the analysts regarding this aspect. This expert system has to
perform automated reasoning and output recommendations (or alerts) about maritime anomalies, thereby supporting the
identification of vessels of interest and threat analysis. The system must contribute to a lower false alarm rate and a
better probability of detection in drawing operator's attention to vessels worthy of their attention. It must provide
explanations as to why the vessels may be of interest, with links to resources that help the operators dig deeper.
Mechanisms are necessary for the analysts to fine tune the system, and for the knowledge engineer to maintain the
knowledge base as the expertise of the operators evolves. This paper portrays the anomaly detection prototype, and
describes the knowledge acquisition and elicitation session conducted to capture the know-how of the experts, the formal
knowledge representation enablers and the ontology required for aspects of the maritime domain that are relevant to
anomaly detection, vessels of interest, and threat analysis, the prototype high-level design and implementation on the
service-oriented architecture of the CKEF, and other findings and results of this ongoing activity.