OKG-LLM: Aligning Ocean Knowledge Graph with Observation Data via LLMs for Global Sea Surface Temperature Prediction

2025-07-30

AI Summary

Existing sea surface temperature (SST) forecasting methods suffer from insufficient integration of oceanic domain knowledge, limited generalizability, and poor interpretability. To address these challenges, we propose OKG-LLM: a novel framework that (1) constructs the first ocean knowledge graph (OKG) specifically designed for SST prediction, and (2) introduces a multimodal fusion architecture synergizing knowledge graph embedding with large language models (LLMs) to semantically align and jointly model structured domain knowledge with fine-grained spatiotemporal observational data. By integrating graph embedding, LLM fine-tuning, and multi-source data fusion, OKG-LLM significantly improves prediction accuracy and robustness, outperforming state-of-the-art data-driven and physics-based models across multiple real-world SST datasets. The implementation is open-sourced, establishing an interpretable and scalable paradigm for intelligent ocean environmental forecasting.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Sea surface temperature (SST) prediction is a critical task in ocean science, supporting various applications, such as weather forecasting, fisheries management, and storm tracking. While existing data-driven methods have demonstrated significant success, they often neglect to leverage the rich domain knowledge accumulated over the past decades, limiting further advancements in prediction accuracy. The recent emergence of large language models (LLMs) has highlighted the potential of integrating domain knowledge for downstream tasks. However, the application of LLMs to SST prediction remains underexplored, primarily due to the challenge of integrating ocean domain knowledge and numerical data. To address this issue, we propose Ocean Knowledge Graph-enhanced LLM (OKG-LLM), a novel framework for global SST prediction. To the best of our knowledge, this work presents the first systematic effort to construct an Ocean Knowledge Graph (OKG) specifically designed to represent diverse ocean knowledge for SST prediction. We then develop a graph embedding network to learn the comprehensive semantic and structural knowledge within the OKG, capturing both the unique characteristics of individual sea regions and the complex correlations between them. Finally, we align and fuse the learned knowledge with fine-grained numerical SST data and leverage a pre-trained LLM to model SST patterns for accurate prediction. Extensive experiments on the real-world dataset demonstrate that OKG-LLM consistently outperforms state-of-the-art methods, showcasing its effectiveness, robustness, and potential to advance SST prediction. The codes are available in the online repository.

Problem

• Integrating ocean knowledge with SST prediction using LLMs
• Addressing neglect of domain knowledge in current SST methods
• Aligning Ocean Knowledge Graph with numerical SST data

Innovation

• Constructs Ocean Knowledge Graph for SST prediction
• Embeds semantic and structural ocean knowledge
• Aligns knowledge graph with numerical SST data