CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform

2026-02-09

AI Summary

This work addresses the challenge non-expert users face in effectively interpreting complex and incomplete AIS vessel trajectory data. To this end, the paper proposes a knowledge-centric trajectory analysis platform that uniquely integrates large language models (LLMs) with structured-data knowledge graphs (SD-KGs) to enable automatic trajectory completion, semantic annotation, and enhanced interpretability. The platform supports interactive parameter configuration, allowing users to intuitively compare original and completed trajectories and trace the underlying knowledge sources justifying the results. By synergizing symbolic knowledge representation with LLM-driven reasoning, the approach significantly improves the efficiency, transparency, and explorability of trajectory data analysis for domain-non-specialist users.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Planning, Routing, and Scheduling: Planning with Language Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Vessel trajectory data from the Automatic Identification System (AIS) is used widely in maritime analytics. Yet, analysis is difficult for non-expert users due to the incompleteness and complexity of AIS data. We present CLEAR, a knowledge-centric vessel trajectory analysis platform that aims to overcome these barriers. By leveraging the reasoning and generative capabilities of Large Language Models (LLMs), CLEAR transforms raw AIS data into complete, interpretable, and easily explorable vessel trajectories through a Structured Data-derived Knowledge Graph (SD-KG). As part of the demo, participants can configure parameters to automatically download and process AIS data, observe how trajectories are completed and annotated, inspect both raw and imputed segments together with their SD-KG evidence, and interactively explore the SD-KG through a dedicated graph viewer, gaining an intuitive and transparent understanding of vessel movements.

Problem

• AIS data
• vessel trajectory analysis
• data incompleteness
• data complexity
• non-expert users

Innovation

• Knowledge-Centric
• Large Language Models
• AIS Trajectory Completion
• Structured Data-derived Knowledge Graph
• Interactive Maritime Analytics