Construction of a Large-Scale Maritime Elements Semantic Schema Based on Heterogeneous Graph Models

From the perspective of optimizing maritime logistics, a key focus in the field of maritime information research has been how to extract behavioral patterns and deep behavioral characteristics of vessels from vast amounts of shipping statistics. Additionally, aligning these characteristics with infrastructure such as berths for effective association and recommendation to vessels is a critical requirement for the evolution of intelligent maritime systems. Traditional methods primarily focus on the behavioral trajectories of vessel navigation, failing to explore the geographical interconnections between vessels and port infrastructure. In light of this, this paper proposes a framework for deep mining of shipping information based on knowledge graph technology. Utilizing AIS data and spatial data of port facilities, it constructs a semantic relationship in the form of triplets between vessels, berths, and waterways, and semantically models vessel behaviors. Effective identification of vessels is achieved based on various semantic information. Simultaneously, based on the berthing semantic relationship between vessels and berths, a reverse semantic knowledge graph of berths is constructed with respect to vessel type, size, and class. This study compares different graph embedding methods, dimensionality reduction techniques, and classification approaches to achieve optimal experimental results. The findings indicate that the vessel type recognition accuracy in the proposed framework reached 83.1%, and the number of Identical Relationships between the recommended and original berths in similar berth recommendations was 3.755. The experiments demonstrate that the framework can provide a technical foundation for deep mining of vessel behavior, vessel type identification, and berth recommendation, as well as a semantic basis for large-scale maritime models.