摘要
传统的P2P系统基于单特征词搜索,且不支持语义,有一定的局限性。向量空间模型VSM技术的应用解决了P2P系统中多特征词搜索的问题;标识符空间的分割,使相似文档在邻近的节点范围内聚集,提高了搜索的速度;语义思想的应用,使P2P系统能够理解搜索请求,有利于检索性能,特别是查全率的提高。仿真实验的结果表明:实现了多特征词的搜索;搜索收敛的速度较快;支持语义,检索性能得到了提高;节点达到了较好的负载平衡。
The conventional P2P system has its limitations in that it is based on single keyword search and does not support semantics.With Vector Space Model,this paper attempts to realize the multi-keywords search ;the segmentation of identifier space helps to gather the similar files at adjacent nodes,thus can speed up search process.In addition,semantic idea assists P2P system better understanding the search and improving its performance,especially recall rate.The simulation experiments have indicated： multi-keywords query is realized;query process is accelerated;search performance is improved by using semantic technology;a better load balance is achieved.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第3期8-11,共4页
Computer Engineering and Applications
基金
国家自然科学基金资助项目(90204010)。
关键词
对等网
语义
多特征词搜索
向量空间模型
负载平衡
Peer to Peer
semantic
multi-keywords search
Vector Space Model
load balance
