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Effective XML keyword search with relevance oriented ranking


Bao, Z and Ling, TW and Chen, B and Lu, J, Effective XML keyword search with relevance oriented ranking, Proceedings of the 25th IEEE International Conference on Data Engineering, 29 March - 2 April 2009, Shanghai, China, pp. 517-528. ISBN 978-076953545-6 (2009) [Refereed Conference Paper]

Copyright Statement

Copyright 2009 IEEE

DOI: doi:10.1109/ICDE.2009.16


    Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: (1) Identify the user search intention, i.e. identify the XML node types that user wants to search for and search via. (2) Resolve keyword ambiguity problems: a keyword can appear as both a tag name and a text value of some node; a keyword can appear as the text values of different XML node types and carry different meanings. (3) As the search results are sub-trees of the XML document, new scoring function is needed to estimate its relevance to a given query. However, existing methods cannot resolve these challenges, thus return low result quality in term of query relevance.
   In this paper, we propose an IR-style approach which basically utilizes the statistics of underlying XML data to address these challenges. We first propose specific guidelines that a search engine should meet in both search intention identification and relevance oriented ranking for search results. Then based on these guidelines, we design novel formulae to identify the search for nodes and search via nodes of a query, and present a novel XML TF*IDF ranking strategy to rank the individual matches of all possible search intentions. Lastly, the proposed techniques are implemented in an XML keyword search engine called XReal, and extensive experiments show the effectiveness of our approach.

Item Details

Item Type:Refereed Conference Paper
Keywords:information retrieval, unstructured data, semi-structured data
Research Division:Information and Computing Sciences
Research Group:Data management and data science
Research Field:Data management and data science not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Bao, Z (Dr Zhifeng Bao)
ID Code:92210
Year Published:2009
Web of Science® Times Cited:55
Deposited By:Information and Communication Technology
Deposited On:2014-06-10
Last Modified:2015-03-26

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