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Research on river geomorphology knowledge extraction based on large language models
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Xiaolu LIU1, Jiahe WANG1, 2, Jin WU1, Xiaoyi MA1, 2, Shuai QIN3, Shu WANG1, 2, 4, *
Science & Technology Review | 2026, 44(17) : 102 - 113
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Science & Technology Review | 2026, 44(17): 102-113
• Papers •
Research on river geomorphology knowledge extraction based on large language models
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Xiaolu LIU1, Jiahe WANG1, 2, Jin WU1, Xiaoyi MA1, 2, Shuai QIN3, Shu WANG1, 2, 4, *
Affiliations
  • 1State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Hebei South Canal River Affairs Center, Cangzhou 061001, China
  • 4Jiangsu Center for Collaborative innovation in Geographical information Resource Development and Application, Nanjing 210023, China
Published: 2026-09-13 doi: 10.3981/j.issn.1000-7857.2025.06.00046
Outline
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As river geomorphology research increasingly relies on large−scale literature integration and analysis, the challenge of extracting structured knowledge from unstructured texts has become central to advancing digital and semantic approaches in geomorphological science. This study proposes a GPT−based method for automatic knowledge extraction in the field of fluvial geomorphology. A structured Few−shot prompt framework was designed to guide the model in identifying geomorphic entities and their relations, enabling the generation of standardized knowledge triples in the form of <entity1, relation, entity2>. The workflow consists of corpus preparation, prompt engineering, model invocation, and structured output. Applied to 30 high−quality English−language journal articles, the approach extracted a total of 7605 knowledge triples, encompassing 22815 entities and relations. Evaluation results demonstrate that the method achieves high accuracy (Precision=0.834), good recall (Recall=0.789), and stable overall performance (F1=0.862). Nonetheless, limitations remain in the handling of abstract logic, long−range dependencies, and relation directionality. Future improvements may involve domain−specific model fine−tuning, integration of geographic ontologies, and multimodal data fusion. This research provides a practical solution for intelligent knowledge construction in fluvial geomorphology and expands the methodological toolkit at the intersection of geographic information science and artificial intelligence.

fluvial geomorphology  /  large language models  /  knowledge extraction  /  triple structure  /  GPT  /  intelligent physical geography
刘晓璐, 王嘉嚇, 吴进, 马晓熠, 秦帅, 王曙. 基于大语言模型的河流地貌知识抽取. 科技导报, 2026 , 44 (17) : 102 -113 . DOI: 10.3981/j.issn.1000-7857.2025.06.00046
Xiaolu LIU, Jiahe WANG, Jin WU, Xiaoyi MA, Shuai QIN, Shu WANG. Research on river geomorphology knowledge extraction based on large language models[J]. Science & Technology Review, 2026 , 44 (17) : 102 -113 . DOI: 10.3981/j.issn.1000-7857.2025.06.00046
Year 2026 volume 44 Issue 17
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252
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Article Info
doi: 10.3981/j.issn.1000-7857.2025.06.00046
  • Receive Date:2025-06-11
  • Online Date:2026-10-14
  • Published:2026-09-13
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  • Received:2025-06-11
  • Revised:2025-10-26
Funding
Affiliations
    1State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
    3Hebei South Canal River Affairs Center, Cangzhou 061001, China
    4Jiangsu Center for Collaborative innovation in Geographical information Resource Development and Application, Nanjing 210023, China
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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