基于知识库的智能问答系统构建技术研究OA

With the rapid development of information technology,data centers have accumulated massive amounts of data.How to efficiently obtain valuable information from this data has become a key issue restricting data centers from exerting their military effectiveness.To address this issue,considering that intelligent question-answering systems,as tools capable of understanding users' natural language questions and pro-viding accurate answers,have broad application prospects in the field of data centers,this paper proposes a construction technology for intelli-gent question-answering systems based on data center knowledge bases.First,focusing on users' diverse data needs in different application sce-narios within the military field,a data retrieval demand model is established.Second,for data retrieval demand models of different dimensions,a diverse knowledge retrieval correlation algorithm is proposed.Parameters such as correlation degree and recommendation degree between re-trieval demands and candidate datasets are defined.Based on these parameters,the matching degree between candidate datasets and retrieval demands is calculated to find the most relevant candidate datasets.Then,the generated candidate datasets and users' retrieval demands are transmitted to the large language model together,and high-quality answers are generated through the retrieval-augmented generation algorithm.Finally,verified through experiments,this technology has shown good performance in answering data center-related questions,which helps im-prove the comprehensive utilization level and service quality of multi-source knowledge bases in data centers.