Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model

ZHENG Hao; YANG Guowei; LI Fei; GUO Jianlin

doi:10.11959/j.issn.2096-6652.202532

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Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model

Papers and Reports | 更新时间：2025-11-18

- Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 3, Pages: 361-369(2025)
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.浙江省淡水水产研究所，浙江湖州 313001
- 作者简介：
- 基金信息：
  
  "Three Rural and Nine Party" Science and Technology Cooperation Plan of Zhejiang Province(2023SNJF073)
- DOI：10.11959/j.issn.2096-6652.202532
  CLC： TP391
- Received：07 April 2025，
  
  Revised：2025-06-23，
  
  Accepted：17 July 2025，
  
  Published：15 September 2025
- 稿件说明：
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郑豪,杨国伟,李飞等.融合知识图谱和大语言模型的罗氏沼虾养殖问答系统的设计与实现[J].智能科学与技术学报,2025,07(03):361-369.

ZHENG Hao,YANG Guowei,LI Fei,et al.Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):361-369.
郑豪,杨国伟,李飞等.融合知识图谱和大语言模型的罗氏沼虾养殖问答系统的设计与实现[J].智能科学与技术学报,2025,07(03):361-369. DOI： 10.11959/j.issn.2096-6652.202532.

ZHENG Hao,YANG Guowei,LI Fei,et al.Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):361-369. DOI： 10.11959/j.issn.2096-6652.202532.

摘要

互联网存储着大量罗氏沼虾的相关信息，借助网络搜索、大语言模型（large language model，LLM）很难高效获取养殖问题的准确解答。基于实体粒度的属性拼接向量检索策略，设计并实现了一个面向罗氏沼虾养殖行业的智能问答系统。该系统主要通过融合知识图谱和大语言模型来实现罗氏沼虾养殖领域的友好问答。首先，借助LLM来完成知识抽取功能，将收集到的原始数据转化为结构化数据，构建一个罗氏沼虾养殖相关的知识图谱，将图谱中实体名称与其属性拼接形成语义丰富的描述文本，进行向量化表示并导入Neo4j。然后，通过LLM搭建一个问题过滤模块，对用户提出的问题先进行语义优化和分类后进行向量化处理，从而提升噪声环境下的问答鲁棒性。最后，通过语义向量检索方式，将检索到的知识传递给LLM进行专业回答，从而实现融合知识图谱和LLM的智能问答系统。实验测试展示了所提方法可以有效提高回答的准确性和鲁棒性。该系统有望助力于提供高效、准确、友好的罗氏沼虾专业信息检索，以期提升养殖决策效率和增加养殖生产效益。

Abstract

As a vast amount of information about Macrobrachium rosenbergii is available on the Internet

it makes the use of web searches or general-purpose large language model (LLM) inefficient to obtain accurate answers to aquaculture-specific questions. An intelligent question answering (QA) system for the Macrobrachium rosenbergii aquaculture field based on an entity-level attribute-concatenated vector retrieval strategy was proposed and implemented. A knowledge graph with LLM was integrated to support professional

user-friendly QA. Firstly

raw

unstructured aquaculture data was transformed into structured triples by an LLM-based knowledge extraction module to construct a knowledge graph. The entity names and their attributes were concatenated into semantically rich textual descriptions

which were vectorized and stored in a Neo4j database for similarity-based retrieval. Then

a question filtering module based on an LLM was introduced to optimize and classify user queries before vectorization

thereby enhancing answer robustness in noisy or ambiguous input environments. Finally

semantic vector retrieval was performed over the knowledge graph

and the retrieved knowledge was passed to an LLM for generating professional

context-aware responses. Experimental results demonstrate that the proposed approach effectively improves response accuracy and robustness. The system shows promise in enabling efficient

accurate

and user-friendly knowledge access in aquaculture

potentially improving decision-making and enhancing production outcomes.

关键词

Keywords

references

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