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EXTRUCT: Using Deep Structural Information in XML Keyword Search

Summary: EXTRUCT exploits deep XML structural correlations for keyword search via information-theoretic NTC and NTPC relevance measures. Its structure-aware ranking substantially improves precision and recall over prior XML keyword-search methods on real-world datasets. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10196
Venue
VLDB
Year
2010
Pagerank
5.6723214e-05
Overall Rank
7,209 | 50.55%
DOI
10.14778/1920841.1920859

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{termehchy_vldb10,
        title = {{EXTRUCT: Using Deep Structural Information in XML Keyword Search}},
        author = {Termehchy, Arash and Winslett, Marianne},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {2},
        pages = {1593--1596},
        doi = {10.14778/1920841.1920859},
        url = {https://doi.org/10.14778/1920841.1920859},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
2,946 Gestural Query Specification 2014 VLDB 7.93259e-05
7,281 A General-Purpose Query-Centric Framework for Querying Big Graphs 2016 VLDB 5.6567845e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
77 XRANK: Ranked Keyword Search over XML Documents 2003 SIGMOD 0.00037048607
399 XSEarch: A Semantic Search Engine for XML 2003 VLDB 0.00019177959
795 Efficient Keyword Search for Smallest LCAs in XML Databases 2005 SIGMOD 0.00013939786
4,408 Reasoning and Identifying Relevant Matches for XML Keyword Search 2008 VLDB 6.7190081e-05
Previous Page 1 / 1 Next

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