GLM-OOTKG: an LLM-driven framework for constructing knowledge graphs in ocean observation technology

Despite the accumulation of vast amounts of ocean observation data and diverse instrumentation, a critical gap persists in modern oceanography: instruments and their generated heterogeneous datasets are routinely treated as isolated entities, lacking a systematic knowledge graph to map the overarching observation technology ecosystem. This systemic disconnection creates a severe semantic gap between raw instrument specifications and the geophysical parameters they measure, ultimately hindering automated cross-platform interoperability and intelligent data discovery. To bridge this gap, we introduce GLM-OOTKG(GLM-driven Ocean Observation Technology Knowledge Graph), a Large Language Model (LLM)-based framework designed to construct a comprehensive knowledge graph for ocean observation technologies. Specifically, we propose a constraint-based prompting strategy that integrates predefined domain ontologies, few-shot examples, and explicit null-response instructions. This strategy effectively mitigates LLM hallucinations, enabling the accurate extraction of complex technical relationships among instruments, specifications, and variables from unstructured literature. Furthermore, we develop a dual-weighted ranking algorithm built upon Transformer embeddings to reconcile terminological inconsistencies and achieve precise entity alignment across heterogeneous documents. Evaluated on a curated corpus of oceanographic technical literature, GLM-OOTKG achieves F1-scores of 88.9% in entity extraction and 87.0% in relationship extraction, significantly outperforming unconstrained baselines by absolute margins of 9.0% and 13.4%, respectively. Ultimately, the resulting knowledge graph serves as a robust semantic bridge between observation hardware and derived data products, providing a structured, machine-readable metadata foundation that conceptually supports future downstream tasks, such as multi-source data assimilation and cross-platform synergies within the Space–Air–Ground–Ocean observation network.