湖南农业大学学报（自然科学版）2025，Vol.51Issue(3)：97-109,13.DOI:10.13331/j.cnki.jhau.2025.03.013
融合知识图谱和语义信息的烟叶分级问答系统
Tobacco grading question answering system integrating knowledge graph and semantic information
摘要
Abstract
In view of the redundancy of knowledge in the field of tobacco grading and the absence of a professional platform for academic retrieving,the knowledge graph of tobacco grading was constructed by collecting multi-source tobacco grading data and combining the top-down method,and an intelligent question and answer system was developed on this basis.The core technologies are as follows.1)Collecting tobacco leaf grading data through named entity recognition(NER)and relation extraction(RE)to extract triplet information,and import it into the Neo4j platform for storage.2)For question semantic parsing,the BERT-BiGRU-MHSA-CRF model fused with graph data was used to improve the entity recognition effect of question sentences,and the self-attention mechanism was integrated into the BERT-TextCNN model to parse user hierarchical intent.Then,the cypher query statement was automatically constructed by matching the template and replacing the slot information,and the most accurate answer was retrieved and returned in the Neo4j knowledge base.The results showed that the constructed knowledge graph contains 6 620 entities and more than 14 000 relationships.The harmonic mean F1 of the question entity recognition model BERT-BiGRU-MHSA-CRF was 94.12%,and the F1 of the hierarchical intent recognition model BERT-TextCNN-Attention was 98.77%.In summary,the system can quickly retrieve and accurately answer multiple types of questions related to tobacco grading,which can provide auxiliary functions for graders.关键词
领域知识图谱/语义解析/问答系统/烟叶分级/问句实体识别/意图识别Key words
domain knowledge graph/semantic analysis/question answering system/tobacco grading/question entity recognition/intent recognition分类
轻工纺织引用本文复制引用
陈婷,朱昌群..融合知识图谱和语义信息的烟叶分级问答系统[J].湖南农业大学学报（自然科学版）,2025,51(3):97-109,13.基金项目
国家自然科学基金项目(61761024) （61761024）
