摘要
【目的/意义】贝叶斯网络是描述随机变量之间依赖关系的图形模式,被广泛应用于不确定性问题的智能求解。【方法/过程】文章介绍了信息检索领域中基于贝叶斯网络技术的三种检索模型-推理网络模型、信任度网络模型、贝叶斯网络检索模型,详细地分析了其工作原理、论述了国内外研究者使用贝叶斯网络在信息检索领域的研究现状,探讨了每种模型的优势与不足。【结果/结论】指出了贝叶斯网络技术在信息检索领域的发展趋势.
【Purpose/significance】Bayesian network is graphical model to describe dependencies between random variables and is widely used to solve uncertainty problems in intelligent.【Method/process】Article introduces three kinds of retrieved model, named reasoning network model, belief network model and Bayesian network retrieval model, analyses their working principle detailed, detailedly research status of using Bayesian network in information retrieved field, and discusses the advantage and shortcoming of each model.【Result/conclusion】The future research trend are pointed out about information retrieved field using Bayesian network.
作者
郑伟
侯宏旭
武静
ZHENG Wei;HOU Hong-xu;WU Jing(School of Computer Science, lnner Mongolia University, Hohhot 010021, China;School of Science, Hebei North University, Zhangjiakou 075000, China)
出处
《情报科学》
CSSCI
北大核心
2018年第6期136-141,共6页
Information Science
基金
国家自然科学基金项目(61362028)
张家口市科学技术研究与发展计划项目(1611056)
关键词
贝叶斯网络
信息检索
术语
文档
Bayesian network
information retrieval
term
document
