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Efficient keyword search on large tree structured datasets

Published: 20 May 2012 Publication History

Abstract

Keyword search is the most popular paradigm for querying XML data on the web. In this context, three challenging problems are (a) to avoid missing useful results in the answer set, (b) to rank the results with respect to some relevance criterion and (c) to design algorithms that can efficiently compute the results on large datasets.
In this paper, we present a novel multi-stack based algorithm that returns as an answer to a keyword query all the results ranked on their size. Our algorithm exploits a lattice of stacks each corresponding to a partition of the keyword set of the query. This feature empowers a linear time performance on the size of the input data for a given number of query keywords. As a result, our algorithm can run efficiently on large input data for several keywords. We also present a variation of our algorithm which accounts for infrequent keywords in the query and show that it can significantly improve the execution time. An extensive experimental evaluation of our approach confirms the theoretical analysis, and shows that it scales smoothly when the size of the input data and the number of input keywords increases.

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cover image ACM Conferences
KEYS '12: Proceedings of the Third International Workshop on Keyword Search on Structured Data
May 2012
78 pages
ISBN:9781450311984
DOI:10.1145/2254736
  • General Chairs:
  • Ling Tok Wang,
  • Ge Yu,
  • Jiaheng Lu,
  • Wei Wang
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 May 2012

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Author Tags

  1. LCA
  2. XML
  3. keyword search
  4. ranking
  5. search algorithm
  6. tree-structured data

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  • (2020)Diversified spatial keyword search on RDF dataThe VLDB Journal10.1007/s00778-020-00610-z29:5(1171-1189)Online publication date: 12-Mar-2020
  • (2018)Thematic ranking of object summaries for keyword searchData & Knowledge Engineering10.1016/j.datak.2017.08.002113(1-17)Online publication date: Jan-2018
  • (2015)Top-k-size keyword search on tree structured dataInformation Systems10.1016/j.is.2014.07.00247:C(178-193)Online publication date: 1-Jan-2015
  • (2015)Reasoning with patterns to effectively answer XML keyword queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-015-0384-324:3(441-465)Online publication date: 1-Jun-2015
  • (2014)Exploiting Semantic Result Clustering to Support Keyword Search on Linked DataWeb Information Systems Engineering – WISE 201410.1007/978-3-319-11749-2_34(448-463)Online publication date: 2014
  • (2013)XReason: A Semantic Approach That Reasons with Patterns to Answer XML Keyword QueriesDatabase Systems for Advanced Applications10.1007/978-3-642-37487-6_24(299-314)Online publication date: 2013

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