A Meta-search Method with Clustering and Term Correlation

A meta-search engine propagates user queries to its participant search engines following a server selection strategy. To facilitate server selection, the metasearch engine must keep concise descriptors about the document collections indexed by the participant search engines. Most existing approaches record in the descriptors about what terms appear in a document collection, but they skip the information about which documents a keyword appears in. This results in ineffective server ranking for multi-term queries, because a document collection may contain all of the query terms but not all of the terms appear in the same document. In this paper, we propose a server ranking approach in which each search engine's document collection is divided into clusters by indexed terms. Furthermore, we keep the term correlation information in a cluster descriptor as a concise method to estimate the degree of term co-occurrence in a document set. We emprically show that combining clustering and t...

• Type: Conference
• Year: 2004
• Where: DASFAA
• Authors: Dyce Jing Zhao, Dik Lun Lee, Qiong Luo
• Keywords: DASFAA 2004, Database, Document Collection, Participant Search Engines, Server Selection