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 situational analysis is an important component of current maritime traffic management. However, it faces some challenges and difficulties. Firstly, the heterogeneity and diversity of ship communication data make data integration and analysis difficult. Secondly, traditional data analysis methods often ignore the spatio-temporal relationships and contextual information between data, resulting in inaccurate and incomplete analysis results. In addition, existing ship communication situational analysis methods often only consider single-modal data, which cannot fully reflect the multi-modal characteristics of maritime traffic. To address these issues, we propose a ship communication situational analysis method based on a multi-modal spatio-temporal knowledge graph. This method can effectively integrate heterogeneous data and model and analyze spatio-temporal relationships and contextual information between data, thus achieving more accurate and comprehensive situational analysis. In conclusion, a multi-modal spatio-temporal knowledge graph is an effective method for ship communication situational analysis, which can overcome the current challenges and difficulties and provide more accurate and comprehensive data support for maritime traffic management.
Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to "silos" that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration [5], federated data query [14] and knowledge graph construction [6]. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.
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