Navigation rules are critical for regulating ship behavior, and effective water traffic management requires accurate identification of ships exhibiting abnormal behavior that violates these rules. To address this need, this paper presents an ontology-based method for identifying abnormal ship behavior. First, we analyzed navigation rules (local regulations) to extract key elements. Next, based on this extraction, we built a navigation rule ontology that categorized ship behavior into state behavior (ship behavior at a specific time point) and process behavior (ship behavior in a time interval). We then constructed an abnormal ship behavior ontology, defined using topological relationships and navigation rules. Finally, we constructed inference rules to detect abnormal ship behaviors by using SWRL (Semantic Web Rule Language) and validated the effectiveness of the method with ship instances. The experimental results demonstrate that this method can accurately infer ships’ behaviors that deviate from established navigation rules. This research has significant implications for reducing waterborne traffic accidents, improving navigational safety, and safeguarding maritime traffic.