智能科学与技术学报2025，Vol.7Issue(3)：361-369,9.DOI:10.11959/j.issn.2096-6652.202532
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
Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model
摘要
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 intro-duced to optimize and classify user queries before vectorization,thereby enhancing answer robustness in noisy or ambigu-ous 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 en-abling efficient,accurate,and user-friendly knowledge access in aquaculture,potentially improving decision-making and enhancing production outcomes.关键词
知识图谱/大语言模型/罗氏沼虾养殖/智能问答Key words
knowledge graph/large language model/Macrobrachium rosenbergii aquaculture/intelligent question answering分类
信息技术与安全科学引用本文复制引用
郑豪,杨国伟,李飞,郭建林..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,2025,7(3):361-369,9.基金项目
浙江省"三农九方"科技协作计划项目(No.2023SNJF073)"Three Rural and Nine Party"Science and Technology Cooperation Plan of Zhejiang Province(No.2023SNJF073) （No.2023SNJF073）
