Finding and ranking compact connected trees for effective keyword proximity search in XML documents
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摘要
In this paper, we study the problem of keyword proximity search in XML documents. We take the disjunctive semantics among the keywords into consideration and find top-k relevant compact connected trees (CCTrees) as the answers of keyword proximity queries. We first introduce the notions of compact lowest common ancestor (CLCA) and maximal CLCA (MCLCA), and then propose compact connected trees and maximal CCTrees (MCCTrees) to efficiently and effectively answer keyword proximity queries. We give the theoretical upper bounds of the numbers of CLCAs, MCLCAs, CCTrees and MCCTrees, respectively. We devise an efficient algorithm to generate all MCCTrees, and propose a ranking mechanism to rank MCCTrees. Our extensive experimental study shows that our method achieves both high efficiency and effectiveness, and outperforms existing state-of-the-art approaches significantly.
论文关键词:Lowest common ancestor (LCA),Compact LCA (CLCA),Maximal CLCA (MCLCA),Compact connected trees (CCTrees),Maximal CCTrees (MCCTrees)
论文评审过程:Received 3 April 2008, Revised 20 April 2009, Accepted 31 May 2009, Available online 8 June 2009.
论文官网地址:https://doi.org/10.1016/j.is.2009.05.004