Nearest keyword search on probabilistic XML data

Yue Zhao, Ye Yuan, Guoren Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper pays attention to the nearest keyword (NK) problem on probabilistic XML data (NK-P). NK search occupies an important position in information discovery, information extraction and many other areas. Compared with traditional XML data, it is more expensive to answer NK-P search because of so many possible worlds. NK-P can be seen as an NK problem on many traditional XML documents. For a given node q and a keyword k, an NK-P query returns the node which is nearest to q among all the nodes associated with k in all the possible worlds. NK-P search is not only useful independent operator but also as an important part for keyword search. Firstly, we propose a new NK concept on probabilistic XML data based on possible worlds. Next, we present an indexing algorithm to answer an NK-P query efficiently. Finally, extensive experimental results show that our approach is an effective method on probabilistic XML data, and it could significantly reduce the execution time.

Original languageEnglish
Title of host publicationWeb Technologies and Applications - 16th Asia-Pacific Web Conference, APWeb 2014, Proceedings
PublisherSpringer Verlag
Pages485-493
Number of pages9
ISBN (Print)9783319111155
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event16th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2014 - Changsha, China
Duration: 5 Sept 20147 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8709 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2014
Country/TerritoryChina
CityChangsha
Period5/09/147/09/14

Keywords

  • NK
  • NK-P
  • keyword search
  • possible world
  • probabilistic XML data

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