# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 2 Related Work  
A knowledge graph is a data modeling method that represents knowledge as concepts, entities, and semantic relationships between them in the form of a graph [9]. However, the world contains a vast amount of dynamic and procedural knowledge that conventional static knowledge graphs cannot adequately represent [10]. Spatio-temporal knowledge graphs not only enable the representation of entities but also capture the spatio-temporal changes in those entities. By connecting multi-source spatio-temporal ship communication data and expert knowledge in a graph structure, spatio-temporal knowledge graphs facilitate the dynamic analysis and prediction of communication in monitoring scenarios involving heterogeneous data sources.  
In the research involving the analysis and prediction of maritime communication and activities through the utilization of a knowledge graph, [PERSON] et al. [11] predicted missing nodes in a maritime knowledge graph by link prediction in the knowledge graph. However, they fell short in leveraging temporal and spatial information as guiding factors to achieve more robust predictions. A dynamic method for predicting knowledge graph links was proposed in [12] for identifying navigation scenarios at sea. Dynamic knowledge graphs are used to capture the evolution of entities such as ships, ports, and countries. The study was limited to conducting rudimentary experiments in positional prediction. To achieve the event and attribute predictions as described in the article, substantial reliance on extensive expert knowledge is indispensable. [PERSON] et al. [13] introduced a semantic model of ship behavior (SMSB) to describe the behavior and status of ships on a route, including sailing, anchoring, and stopping. The status is recognized and established by rules, and the potential behavior is inferred by a dynamic Bayesian network (DBN). [PERSON] [5] improved the SEM (Simple Event Model) model based on the core idea of \"process-event-behavior\" and designed a ship activity ontology model. The semantic information of trajectories is extracted using the Stop/Move model and geographic correlation relationship, and the relationship between ship sudden events and normal events is extracted using a deep learning model to complete instance-level filling. [PERSON] et al. [14] used information mining technology to perform spatio-temporal and event correlation analysis on the historical information of ships, forming a knowledge graph analysis system for vertical domain intelligence information on foreign military ship activities. But their works were limited to basic applications such as querying and visualization, without incorporating inferential reasoning into the ship activity knowledge graph.  
Overall, the application of knowledge graph-driven data analysis technology to solve maritime multi-scenario prediction tasks has become an important trend at present, but there is still a lack of research on the management and analysis of ship communication data. Therefore, this paper designs and constructs a multi-modal space-time knowledge graph to uniformly organize and manage large-scale multi-source heterogeneous ship communication data, and provide knowledge support for subsequent data analysis and application.