融合BERT与领域本体规则的农业机械化管理知识图谱构建与智能问答应用研究OA

Aiming to address the issues of fragmented domain knowledge,diverse heterogeneous document formats,and the urgent demand for intelligent decision-making in the field of agricultural machinery management,an automated knowledge extraction and fusion method that combined a BERT pretrained language model with domain ontological rules was proposed,with the aim of constructing a high-quality knowledge base for agricultural machinery management.Firstly,a domain ontology covering categories such as agricultural machinery equipment,maintenance activities,fault diagnosis,and policies and regulations was designed.Secondly,with the help of the BERT pretrained model,entities and relations were accurately extracted from multi-source texts,including monographs on agricultural machinery management,academic literature,technical manuals,and policy and regulatory documents,and the extraction results were validated and de-duplicated by using ontological rules.Finally,the entities,relations,and high-quality triples were loaded into a graph database to support intelligent question answering and decision analysis applications.Experimental results showed that the proportion of high-confidence triples produced by the relation extraction model reached up to 88.9％across different data sources;the intelligent question answering system achieved an accuracy of 90.9％on 450 typical business test cases,with a hallucination rate as low as 3.1％and fully traceable answers,and its performance was significantly better than that of general-purpose large models such as GPT-4o.The system attained an average end-to-end response latency of 150 ms,a throughput of 200 req/s,and kept resource utilization within a reasonable range.This method not only enabled automated and efficient integration of knowledge in the field of agricultural machinery management,filling a gap in related research,but also provided a replicable and continuously evolvable technical path for decision support in smart agricultural machinery.