SemLAV: Querying Deep Web and Linked Open Data with SPARQL

Pauline Folz (1, 2) , Gabriela Montoya (2) , Hala Skaf-Molli (2) , Pascal Molli (2) , Maria-Esther Vidal (3)

1 NANTES METROPOLE
2 GDD - Gestion de Données Distribuées [Nantes]
3 USB - Universidad Simon Bolivar

SemLAV allows to execute SPARQL queries against the Deep Web and Linked Open Data data sources. It implements the mediator-wrapper 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 strat-egy is designed to deliver results quickly based only on view definitions, i.e., no statistics, nor probing on sources are required. In this demonstra-tion, we validate the effectiveness of SemLAV approach with real data sources from social networks and Linked Open Data. We show in differ-ent setups that materializing only a subset of ranked relevant views is enough to deliver significant part of expected results.

Mots clés: Source Selection; Source Ranking; Incremental Query Answering; Deep Web; Linked Open Data

Domaines: Web

Pauline Folz, Gabriela Montoya, Hala Skaf-Molli, Pascal Molli, Maria-Esther Vidal. SemLAV: Querying Deep Web and Linked Open Data with SPARQL. ESWC: Extended Semantic Web Conference, May 2014, Anissaras/Hersonissou, Greece. pp.332 - 337, ⟨10.1007/978-3-319-11955-7_44⟩. ⟨hal-01089881⟩