摘要
为在有组织对等(P2P)系统上提供有效的多关键词查询和搜索机制,提出了多维潜在语义查询(MLSQ)方法.该方法利用Hilbert空间填充曲线(HSFC)和潜在语义索引,将高维语义空间中相似的数据对象映射到一维数值空间中的邻近区域中,并以每个数据对象在一维数值空间中的序号作为键,将数据对象的索引分布到有组织P2P网络邻近的结点上.通过将HSFC中数据点的查找过程嵌入到有组织P2P网络的结点上,再利用网络的数据查找机制,MLSQ可方便地搜索到符合查询要求的数据对象.实验结果表明,MLSQ在搜索过程中需要访问的网络结点数比较少,并具有较高的查准率和较低的通信量的特点.
A new method called multidimensional latent semantic query （MLSQ） was presented in order to provide efficient muhi-keyword query and search mechanism in structured P2P （peer-to-peer） systems. With Hilbert space filling curve （HSFC） and latent semantic index （LSI）, MLSQ maps similar data objects in high dimension semantic space into an adjacent range in one dimensional value space, and considers every data object sequence number in one dimensional space as a key. MLSQ distributes the keys to adjacent nodes of structured P2P systems. By embedding the process of searching data points of HSFC into the nodes of structured P2P networks and using the data search mechanism of networks, MLSQ can facilitate to search the data objects in accordance with query requirements. The theoretical analysis and experimental results show that MLSQ can improve the query accuracy and reduce the number of communication messages, so MLSQ is better than the existing multidimensional query based on keywords matching in structured P2P networks.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2005年第10期1064-1067,1071,共5页
Journal of Xi'an Jiaotong University
基金
国家高技术研究发展计划资助项目(2003AA1Z2610)
关键词
有组织对等系统
空间填充曲线
多维潜在语义
语义查询
structured peer-to-peer system
space fillcing curve
multidimensional latent semantic
semantic query
