DFS-based frequent graph pattern extraction to characterize the content of RDF Triple Stores

Abstract : Semantic web applications often access distributed triple stores relying on different ontologies and maintaining bases of RDF annotations about different domains. Use cases often involve queries which results combine pieces of annotations distributed over several bases maintained on different servers. In this context, one key issue is to characterize the content of RDF bases to be able to identify their potential contributions to the processing of a query. In this paper we propose an algorithm to extract a compact representation of the content of an RDF repository. We first improve the canonical representation of RDF graphs based on DFS code proposed in the literature. We then provide a join operator to significantly reduce the number of frequent graph patterns generated from the analysis of the content of the base, and we reduce the index size by keeping only the graph patterns with maximal coverage. Our algorithm has been tested on different data sets as discussed in conclusion.
Type de document :
Communication dans un congrès
Web Science Conference 2010 (WebSci10), Apr 2010, Raleigh, United States. <http://www.websci10.org/home.html>
Liste complète des métadonnées


https://hal.inria.fr/hal-01170896
Contributeur : Fabien Gandon <>
Soumis le : jeudi 2 juillet 2015 - 14:46:03
Dernière modification le : mercredi 14 décembre 2016 - 01:07:23
Document(s) archivé(s) le : mardi 25 avril 2017 - 21:56:56

Fichier

BasseEtAl_WebScience_2011_Auth...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01170896, version 1

Collections

Citation

Adrien Basse, Fabien Gandon, Isabelle Mirbel, Moussa Lo. DFS-based frequent graph pattern extraction to characterize the content of RDF Triple Stores. Web Science Conference 2010 (WebSci10), Apr 2010, Raleigh, United States. <http://www.websci10.org/home.html>. <hal-01170896>

Partager

Métriques

Consultations de
la notice

223

Téléchargements du document

481