Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-40285-2_5
Title: Efficient XML keyword search: From graph model to tree model
Authors: Zeng, Y.
Bao, Z.
Ling, T.W. 
Li, G.
Issue Date: 2013
Citation: Zeng, Y.,Bao, Z.,Ling, T.W.,Li, G. (2013). Efficient XML keyword search: From graph model to tree model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8055 LNCS (PART 1) : 25-39. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-40285-2_5
Abstract: Keyword search, as opposed to traditional structured query, has been becoming more and more popular on querying XML data in recent years. XML documents usually contain some ID nodes and IDREF nodes to represent reference relationships among the data. An XML document with ID/IDREF is modeled as a graph by existing works, where the keyword query results are computed by graph traversal. As a comparison, if ID/IDREF is not considered, an XML document can be modeled as a tree. Keyword search on XML tree can be much more efficient using tree-based labeling techniques. A nature question is whether we need to abandon the efficient XML tree search methods and invent new, but less efficient search methods for XML graph. To address this problem, we propose a novel method to transform an XML graph to a tree model such that we can exploit existing XML tree search methods. The experimental results show that our solution can outperform the traditional XML graph search methods by orders of magnitude in efficiency while generating a similar set of results as existing XML graph search methods. © 2013 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/78121
ISBN: 9783642402845
ISSN: 03029743
DOI: 10.1007/978-3-642-40285-2_5
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