DT4GS Knowledge Graph

The Knowledge Graph (KG) is designed with a focus on IMO number, ship name, and ship type as central identifiers, ensuring that every node representing a vessel is anchored by these unique values. This design choice allows for a clear and consistent visual representation where each ship is easily identifiable, and all associated data is linked directly to these identifiers. In the Neo4j graph, the visualization is structured so that nodes and relationships form an interconnected web of information. The graph includes nodes for CO₂ emissions, fuel usage, KPIs, time information, and basic registry details. The right-hand panel in the Neo4j interface is particularly useful; it displays detailed properties of any selected node. For example, when a user selects a node representing CO₂ emissions, the panel highlights specific data points such as annual CO₂ emissions per distance traveled and per unit of work performed. This detailed view is essential for understanding the environmental performance of a vessel. It provides stakeholders with an at-a-glance summary of key performance metrics and allows them to drill down into the data for further analysis. The visual structure of the Knowledge Graph also reinforces the interconnected nature of the data. The graph not only displays nodes but also shows the relationships between them, which represent how different aspects of a vessel’s performance are linked. For instance, a ship node (BasicInfo) is visually connected to multiple other nodes, such as Fuel, CO₂, TimeInfo, KPI, and CargoTransport. Since IMO number, ship name, and ship type serve as the primary unique identifiers, they act as the backbone that links all related data tables and relationships within the graph. This interconnected layout makes it immediately apparent which vessels have similar performance profiles, and which operational metrics are closely correlated. By leveraging Neo4j’s powerful visualization capabilities, the KG transforms static data into a dynamic and interactive tool for real-time decision-making.