Gold Deposit Ontology Guides Large Language Model to Transform Text into Knowledge Graphs for Gold Deposits

The rise of artificial intelligence has led to the emergence of geoscience knowledge graphs (GeoKG) as effective tools for organizing and representing complex knowledge. The growing complexity of geoscience data calls for innovative strategies for structuring and interpreting extensive information. Conventional knowledge extraction methods often rely on manual annotation and deep learning techniques, which can be costly and inefficient. Herein, we leverage a large language model (LLM) to address the challenges of knowledge extraction and fusion in creating a knowledge graph focused on gold deposits. First, we developed an ontology explicitly designed for gold deposits, drawing on insights from geological experts. Next, we formulate a prompt to guide the LLM to accurately extract geological entities and their semantic relationships in accordance with the knowledge graph schema. Subsequently, we conducted geological entity alignment and integration to construct the gold deposit knowledge graph, which encompasses over 3738 entities and 3900 semantic relationships. Finally, we identified an optimal configuration balancing F1-score and computational cost through comparative experiments on locally deployed models with varying parameters. Our findings demonstrate that an LLM can effectively capture long-range contextual relationships to identify geological entities and their semantic connections, demonstrating strong performance in handling diverse expressions.