Research on intelligent services for knowledge about the South China Sea shellfish based on ChatGPT

ZHANG Jiangliang; PU Qiumei; LUO Xun; LI Da

doi:10.11959/j.issn.2096-3750.2025.00421

您当前的位置：

首页 >

文章列表页 >

Research on intelligent services for knowledge about the South China Sea shellfish based on ChatGPT

Theory and Technology | 更新时间：2025-04-21

- Research on intelligent services for knowledge about the South China Sea shellfish based on ChatGPT
- Chinese Journal on Internet of Things Vol. 9, Issue 1, Pages: 138-149(2025)
- 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：
- 基金信息：
  
  The National Social Science Fund of China(20BGL251)
- DOI：10.11959/j.issn.2096-3750.2025.00421
  CLC： G203;
- Received：07 April 2024，
  
  Revised：2024-09-13，
  
  Published：30 March 2025
- 稿件说明：
移动端阅览
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

references

杨文 . 中国南海经济贝类原色图谱 [D ] . 湛江：广东海洋大学 , 2014 .

YANG W . Color atlas of economic mollusks in the South China Sea [D ] . Zhanjiang ： Guangdong Ocean University , 2014 .

罗训 . 新一代信息技术之虚拟现实助力一带一路 [C ] // 2021国际产学研用合作会议（南昌）报告摘要选集 . 北京 : 中国计算机学会(CCF) , 2021 : 35 .

LUO X . Virtual reality empowering the belt and road initiative with new generation information technology [C ] // Proceedings of the 2021 International Conference on Industry-Academia-Research-Application Cooperation (Nanchang) . Beijing : China Computer Federation (CCF) , 2021 : 35 .

DEVLIN J . Bert: pre-training of deep bidirectional transformers for language understanding [J ] . arXiv preprint , 2018 , arXiv: 1810. 04805 .

WU T Y , HE S Z , LIU J P , et al . A brief overview of ChatGPT: the history, status quo and potential future development [J ] . IEEE/CAA Journal of Automatica Sinica , 2023 , 10 ( 5 ): 1122 - 1136 .

XU R X , LUO F L , ZHANG Z Y , et al . Raise a child in large language model: towards effective and generalizable fine-tuning [J ] . arXiv preprint , 2021 , arXiv: 2109.05687 .

文森 , 钱力 , 胡懋地 , 等 . 基于大语言模型的问答技术研究进展综述 [J ] . 数据分析与知识发现 , 2024 , 8 ( 6 ): 16 - 29 .

WEN S , QIAN L , HU M D , et al . Review of research progress on question-answering techniques based on large language models [J ] . Data Analysis and Knowledge Discovery , 2024 , 8 ( 6 ): 16 - 29 .

CHE W X , FENG Y L , QIN L B , et al . N-LTP: an open-source neural language technology platform for Chinese [J ] . arXiv preprint , 2020 , arXiv: 2009.11616 .

JI S X , PAN S R , CAMBRIA E , et al . A survey on knowledge graphs: representation, acquisition, and applications [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 , 33 ( 2 ): 494 - 514 .

张文豪 , 徐贞顺 , 刘纳 , 等 . 知识图谱补全方法研究综述 [J ] . 计算机工程与应用 , 2024 , 60 ( 12 ): 61 - 73 .

ZHANG W H , XU Z S , LIU N , et al . Overview of knowledge graph completion methods [J ] . Computer Engineering and Applications , 2024 , 60 ( 12 ): 61 - 73 .

蒋川宇 , 韩翔宇 , 杨文蕊 , 等 . 医学知识图谱研究与应用综述 [J ] . 计算机科学 , 2023 , 50 ( 3 ): 83 - 93 .

JIANG C Y , HAN X Y , YANG W R , et al . Survey of medical knowledge graph research and application [J ] . Computer Science , 2023 , 50 ( 3 ): 83 - 93 .

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv preprint , 2013 , arXiv: 1301.3781 .

洪海蓝 , 李文林 , 杨涛 , 等 . 基于知识图谱的海洋中药智能问答系统的设计与实现 [J ] . 世界科学技术—中医药现代化 , 2023 , 25 ( 6 ): 1935 - 1941 .

HONG H L , LI W L , YANG T , et al . Design and implementation of intelligent question answering system of marine traditional Chinese medicine based on knowledge graph [J ] . Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology , 2023 , 25 ( 6 ): 1935 - 1941 .

XU G X , MENG Y T , QIU X Y , et al . Sentiment analysis of comment texts based on BiLSTM [J ] . IEEE Access , 2019 , 7 : 51522 - 51532 .

刘巨升 , 杨惠宁 , 孙哲涛 , 等 . 面向知识图谱构建的水产动物疾病诊治命名实体识别 [J ] . 农业工程学报 , 2022 , 38 ( 7 ): 210 - 217 .

LIU J S , YANG H N , SUN Z T , et al . Named-entity recognition for the diagnosis and treatment of aquatic animal diseases using knowledge graph construction [J ] . Transactions of the Chinese Society of Agricultural Engineering , 2022 , 38 ( 7 ): 210 - 217 .

冯杨洋 , 汪庆 , 谢旻晖 , 等 . 从BERT到ChatGPT: 大模型训练中的存储系统挑战与技术发展 [J ] . 计算机研究与发展 , 2024 , 61 ( 4 ): 809 - 823 .

FENG Y Y , WANG Q , XIE M H , et al . From BERT to ChatGPT: challenges and technical development of storage systems for large model training [J ] . Journal of Computer Research and Development , 2024 , 61 ( 4 ): 809 - 823 .

RAY S K , SHAALAN K . A review and future perspectives of Arabic question answering systems [J ] . IEEE Transactions on Knowledge and Data Engineering , 2016 , 28 ( 12 ): 3169 - 3190 .

闫悦 , 郭晓然 , 王铁君 , 等 . 问答系统研究综述 [J ] . 计算机系统应用 , 2023 , 32 ( 8 ): 1 - 18 .

YAN Y , GUO X R , WANG T J , et al . Survey on question answering system research [J ] . Computer Systems & Applications , 2023 , 32 ( 8 ): 1 - 18 .

王耀祖 , 李擎 , 戴张杰 , 等 . 大语言模型研究现状与趋势 [J ] . 工程科学学报 , 2024 , 46 ( 8 ): 1411 - 1425 .

WANG Y Z , LI Q , DAI Z J , et al . Current status and trends in large language modeling research [J ] . Chinese Journal of Engineering , 2024 , 46 ( 8 ): 1411 - 1425 .

HAN Z L , WANG J . Knowledge enhanced graph inference network based entity-relation extraction and knowledge graph construction for industrial domain [J ] . Frontiers of Engineering Management , 2024 , 11 ( 1 ): 143 - 158 .

YANG J X , YANG X X , LI R , et al . BERT and hierarchical cross attention-based question answering over bridge inspection knowledge graph [J ] . Expert Systems with Applications , 2023 , 233 : 120896 .

MA K , TIAN M , TAN Y J , et al . Ontology-based BERT model for automated information extraction from geological hazard reports [J ] . Journal of Earth Science , 2023 , 34 ( 5 ): 1390 - 1405 .

CHEN J R , LU Y Q , ZHANG Y , et al . A management knowledge graph approach for critical infrastructure protection: Ontology design, information extraction and relation prediction [J ] . International Journal of Critical Infrastructure Protection , 2023 , 43 : 100634 .

LIU C J , JI X H , DONG Y H , et al . Chinese mineral question and answering system based on knowledge graph [J ] . Expert Systems with Applications , 2023 , 231 : 120841 .

LIU C Y , ZHANG X Y , XU Y , et al . Knowledge graph for maritime pollution regulations based on deep learning methods [J ] . Ocean & Coastal Management , 2023 , 242 : 106679 .

SOUSA D F , COUTO F M . K-RET: knowledgeable biomedical relation extraction system [J ] . Bioinformatics , 2023 , 39 ( 4 ): btad174 .

MIN B N , ROSS H , SULEM E , et al . Recent advances in natural language processing via large pre-trained language models: a survey [J ] . ACM Computing Surveys , 2023 , 56 ( 2 ): 1 - 40 .

TAN Z , LI D W , WANG S , et al . Large language models for data annotation and synthesis: a survey [J ] . arXiv preprint , 2024 : arXiv: 2402.13446 .

刘宇宁 , 范冰冰 . 图数据库发展综述 [J ] . 计算机系统应用 , 2022 , 31 ( 8 ): 1 - 16 .

LIU Y N , FAN B B . Survey on graph database development [J ] . Computer Systems and Applications , 2022 , 31 ( 8 ): 1 - 16 .

张思佳 , 于红 . 大模型在水产养殖病害防治中的创新应用与展望 [J ] . 大连海洋大学学报 , 2024 , 39 ( 3 ): 369 - 382 .

ZHANG S J , YU H . Innovative applications and prospects of large models in disease prevention and control for aquaculture: a review [J ] . Journal of Dalian Ocean University , 2024 , 39 ( 3 ): 369 - 382 .

Views

201

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on Wi-Fi indoor positioning technology based on knowledge graph

Related Author

Jiandong LI

Jingjing YANG

Ming HUANG

Related Institution

School of Information Science and Engineering, Yunnan University

Wireless Innovation Laboratory of Yunnan University

⁰