# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
###### Abstract  
In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph. ship communication; multi-modal heterogeneous data; multi-modal spatio-temporal ontology; multi-modal spatio-temporal knowledge graph +
Footnote †: journal: Applied  
0000-0001-9993-3300/0000-0000-0000-0000-0000-0000-0000-0000-0000-  
Various maritime activities have accumulated a large amount of ship situation data and communication status data, including ship positioning, attribute data and relevant geographic information, which are stored and managed through relational databases. Geographic information data related to ship movement and operation include ship operations and communication environments, natural environmental condition data, and human-related activity data, which are mainly used to accurately understand and monitor maritime activities and further provide significant data-driven technological innovation space for ship communication status analysis. Although many researchers have attempted to apply traditional big data analysis techniques for analyzing and predicting ship communication status based on the communication data in MySQL databases, there are still some limitations. For example, similarity-based methods, Kalman filtering, exponential smoothing, and fuzzy prediction have encountered challenges in parameter adjustment, establishing evaluation functions, limited learning capabilities, and susceptibility to subjective influences. In response to these issues, [PERSON] et al. [2] proposed the analysis method of Support Vector Machines (SVM). This model exhibits fast convergence and the ability to address high-dimensional recognition problems, but it transforms originally simple problems into complex nonlinear regression problems. On the other hand, artificial neural networks possess strong adaptability, autonomous learning capabilities, excellent information retention, and optimization algorithms, making them more suitable for prediction tasks involving unstructured data and considering various interfering factors. However, artificial neural networks suffer from slow convergence and the potential to converge to local optima [3; 4]. Moreover, these traditional methods primarily utilize ship positioning data for data mining and visualization, without considering the analysis of ship spatio-temporal activity processes and behavior patterns, as well as the application of domain knowledge. In addition, maritime ship situation and communication status data, originating from diverse sources, often exhibit challenges in terms of large data volumes, inconsistent formats, and varying descriptions. Also, the voluminous trajectory data for ship activity with low knowledge density, making it difficult to perform in-depth knowledge mining [5].