Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109522
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Type: Journal article
Title: XML keyword search with promising result type recommendations
Author: Li, J.
Liu, C.
Zhou, R.
Wang, W.
Citation: World Wide Web, 2014; 17(1):127-159
Publisher: Springer
Issue Date: 2014
ISSN: 1386-145X
1573-1413
Statement of
Responsibility: 
Jianxin Li, Chengfei Liu, Rui Zhou, Wei Wang
Abstract: Keyword search enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages and XML data schemas. Existing work has addressed the problem of selecting data nodes that match keywords and connecting them in a meaningful way, e.g., SLCA and ELCA. However, it is time-consuming and unnecessary to serve all the connected subtrees to the users because in general the users are only interested in part of the relevant results. In this paper, we propose a new keyword search approach which basically utilizes the statistics of underlying XML data to decide the promising result types and then quickly retrieves the corresponding results with the help of selected promising result types. To guarantee the quality of the selected promising result types, we measure the correlations between result types and a keyword query by analyzing the distribution of relevant keywords and their structures within the XML data to be searched. In addition, relevant result types can be efficiently computed without keyword query evaluation and any schema information. To directly return top-k keyword search results that conform to the suggested promising result types, we design two new algorithms to adapt to the structural sensitivity of the keyword nodes over the keyword search results. Lastly, we implement all proposed approaches and present the relevant experimental results to show the effectiveness of our approach.
Keywords: XML data management; XML keyword query; result type suggestion
Rights: © Springer Science+Business Media New York 2013
RMID: 0030061001
DOI: 10.1007/s11280-012-0198-9
Grant ID: http://purl.org/au-research/grants/arc/DP110102407
http://purl.org/au-research/grants/arc/DP120102627
Appears in Collections:Computer Science publications

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