Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

Abstract

Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.

The datAcron Ontology for the Specification of Semantic Trajectories

“…Existing approaches for the representation of trajectories, either (a) use plain textual annotations instead of semantic associations to features of interest [3], [12], [13], having limitations towards machine-processable information for the purposes of mobility analysis tasks; (b) constrain the types of events that can be used for structuring a trajectory [33], [3], [12] [29]; or (c) make specific assumptions about the constituents of trajectories [32], [29], [13], [20] [15], [17], thus providing limitations to the specification of trajectories at varying levels of abstraction according to needs.…”

“…Beyond representing trajectories only as sequences of episodes, there is no fine association between abstract models of movement and raw data, providing limitations to analysis tasks that need both of them in association. On the other hand, [13], [29] and [32] provide a two-levels analysis where semantic trajectories are lists of semantic sub-trajectories, and each sub-trajectory is a list of spatial points. Authors in [17], based on the two-levels analysis of trajectory models, introduce an ontological pattern for the specification of trajectories.…”

“…However, it is not clear how multiple models of a single trajectory -each at a different level of analysis-connected to a single episode, are associated. Contextual information in [15] is related to movement models, episodes or semantic trajectories, which is quite generic as a model, while in [32], [29] and [17] fixes and states represent basic behavioural features of the moving object. These may also represent contextual features, and are associated to trajectory points, or in [29] they specify domain-specific features.…”

RTPO: A Domain Knowledge Base for Robot Task Planning

“…It can represent and understand the sophisticated world. Until now, it has been widely employed in various fields such as AI [4,5], Semantic Web [6,7], Information Science [8], etc. Ontology-based task planning is essentially a series of relevant queries and reasoning on ontology knowledge [9].…”

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

“…Similar to the human thinking mode, robots can use knowledge and reasoning to realize smart decision-making. Now, it has been widely used in AI [4,5], semantic web [6,7], informatics [8], and other fields.…”