Personalized best answer computation in graph databases

Michael Ovelgönne, Noseong Park, V. S. Subrahmanian, Elizabeth K. Bowman, Kirk A. Ogaard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Though subgraph matching has been extensively studied as a query paradigm in semantic web and social network data environments, a user can get a large number of answers in response to a query. Just like Google does, these answers can be shown to the user in accordance with an importance ranking. In this paper, we present scalable algorithms to find the top-K answers to a practically important subset of SPARQL-queries, denoted as importance queries, via a suite of pruning techniques. We test our algorithms on multiple real-world graph data sets, showing that our algorithms are efficient even on networks with up to 6M vertices and 15M edges and far more efficient than popular triple stores.

Original languageEnglish (US)
Title of host publicationThe Semantic Web, ISWC 2013 - 12th International Semantic Web Conference, Proceedings
Pages478-493
Number of pages16
EditionPART 1
DOIs
StatePublished - 2013
Externally publishedYes
Event12th International Semantic Web Conference, ISWC 2013 - Sydney, NSW, Australia
Duration: Oct 21 2013Oct 25 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8218 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Semantic Web Conference, ISWC 2013
Country/TerritoryAustralia
CitySydney, NSW
Period10/21/1310/25/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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