基于ChatGPT的中国南海贝类知识智能服务

张蒋良; 蒲秋梅; 罗训; 李达

doi:10.11959/j.issn.2096-3750.2025.00421

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基于ChatGPT的中国南海贝类知识智能服务

理论与技术 | 更新时间：2025-04-21

- 基于ChatGPT的中国南海贝类知识智能服务
- Research on intelligent services for knowledge about the South China Sea shellfish based on ChatGPT
- 物联网学报 2025年9卷第1期页码：138-149
- 作者机构：
  
  1.中央民族大学信息工程学院，北京 100081
  2.民族语言智能分析与安全治理教育部重点实验室，北京 100081
  3.天津理工大学计算机科学与工程学院，天津 300384
  4.国家计算机网络应急技术处理协调中心，北京 100029
- 作者简介：
  
  [ "张蒋良（2000‒ ），男，中央民族大学信息工程学院硕士生，主要研究方向为知识图谱、自然语言处理和数字图像处理。" ]
  [ "蒲秋梅（1976‒ ），女，博士，中央民族大学信息工程学院副教授、硕士生导师，主要研究方向为医学图像处理、自然语言处理和机器学习等。" ]
  [ "罗训（1977‒ ），男，博士，天津理工大学计算机科学与工程学院教授、博士生导师，中国计算机学会（CCF）理事，CCF虚拟现实与可视化专委会主任，智网互联实验室创始人，主要研究方向为数字孪生、虚拟现实、知识图谱和数字图像处理等。" ]
  [ "李达（1998‒ ），男，国家计算机网络应急技术处理协调中心助理工程师，主要研究方向为知识图谱、深度学习、自然语言处理。" ]
- 基金信息：
  
  国家社会科学基金资助项目(20BGL251)
- DOI：10.11959/j.issn.2096-3750.2025.00421
  中图分类号： G203;
- 收稿日期：2024-04-07，
  
  修回日期：2024-09-13，
  
  纸质出版日期：2025-03-30
- 稿件说明：
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张蒋良,蒲秋梅,罗训等.基于ChatGPT的中国南海贝类知识智能服务[J].物联网学报,2025,09(01):138-149.

ZHANG Jiangliang,PU Qiumei,LUO Xun,et al.Research on intelligent services for knowledge about the South China Sea shellfish based on ChatGPT[J].Chinese Journal on Internet of Things,2025,09(01):138-149.
张蒋良,蒲秋梅,罗训等.基于ChatGPT的中国南海贝类知识智能服务[J].物联网学报,2025,09(01):138-149. DOI： 10.11959/j.issn.2096-3750.2025.00421.

ZHANG Jiangliang,PU Qiumei,LUO Xun,et al.Research on intelligent services for knowledge about the South China Sea shellfish based on ChatGPT[J].Chinese Journal on Internet of Things,2025,09(01):138-149. DOI： 10.11959/j.issn.2096-3750.2025.00421.

摘要

南海地区的贝类资源十分丰富，但目前关于这些资源的信息分散在各种书籍和网站中。利用自行训练的深度学习模型进行知识关系的自动抽取可以减少人工整合信息的烦琐工作，但这一过程往往需要大量的数据标注和专家评估，且文本抽取效果的泛化性欠佳。利用ChatGPT构建中国南海贝类的知识服务体系，为上述问题提供了解决方案。通过将ChatGPT与贝类知识提问模板相结合，可以减少对数据集的依赖抽取文本，且效果较为理想；在此基础上，完成了贝类知识图谱的构建及可视化，并利用哈尔滨工业大学语言技术平台（LTP

language technology platform）4.0技术开发了智能问答系统。该应用体系也为人工智能大模型在其他信息搜集和处理方面的应用提供了思路。

Abstract

The South China Sea region is home to abundant shellfish resources

yet the information about these resources is currently dispersed across various books and websites. To ease the manual integration of this information

knowledge extraction processes typically rely on self-trained deep learning models. However

these models often require extensive data labeling and expert evaluation

and their text extraction performance tends to have limited generalization. A solution by utilizing ChatGPT was proposed to build a knowledge service system for shellfish in the South China Sea region. By integrating ChatGPT with query templates for shellfish knowledge

the reliance on labeled datasets for text extraction was significantly reduced

yielding favorable results. On this basis

the construction and visualization of a shellfish knowledge graph was successfully completed

along with the development of an intelligent question-answering system using Harbin Institute of Technology's language technology platform (LTP) 4.0. This research provides valuable insights into the application of large-scale artificial intelligence models for information collection and processing.

关键词

Keywords

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