SemLAV: Querying deep web and linked open data with SPARQL

Pauline Folz*, Gabriela Montoya, Hala Skaf-Molli, Pascal Molli, Maria Esther Vidal

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)

Abstract

SemLAV allows to execute SPARQL queries against the Deep Web and Linked Open Data data sources. It implements the mediatorwrapper architecture based on view definitions over remote data sources. SPARQL queries are expressed using a mediator schema vocabulary, and SemLAV selects relevant data sources and rank them. The ranking strategy is designed to deliver results quickly based only on view definitions, i.e., no statistics, nor probing on sources are required. In this demonstration, we validate the effectiveness of SemLAV approach with real data sources from social networks and Linked Open Data. We show in different setups that materializing only a subset of ranked relevant views is enough to deliver significant part of expected results.

Original languageEnglish
Title of host publicationThe Semantic Web : ESWC 2014 Satellite Events, Revised Selected Papers
EditorsAnna Tordai, Eva Blomqvist, Harald Sack, Raphaël Troncy, Valentina Presutti, Ioannis Papadakis
Number of pages6
PublisherSpringer
Publication date1 Jan 2014
Pages332-337
ISBN (Electronic)9783319119540
DOIs
Publication statusPublished - 1 Jan 2014
Event11th European Semantic Web Symposium on Satellite Events, ESWC 2014 - Ouro Preto, Brazil
Duration: 20 Oct 201422 Oct 2014

Conference

Conference11th European Semantic Web Symposium on Satellite Events, ESWC 2014
Country/TerritoryBrazil
CityOuro Preto
Period20/10/201422/10/2014
SponsorAnnomarket, Big Data Public-Private Forum, Fluid Operations, Forging Online Education through FIRE (FORGE), iSOCO, LDBC: Linked Data Benchmark Council, LinkedTV, LinkedUp, MediaMixer, PlanetData, Prelida: Preserving Linked Data, videolectures.net, XLike, Yahoo! Labs
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8798
ISSN0302-9743

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