About https://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69

Subject: https://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.w3.org/2000/01/rdf-schema#Resourcehttp://www.w3.org/2002/07/owl#Thinghttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingsProperty: http://www.w3.org/2000/01/rdf-schema#labelQuerying Wikidata: Comparing SPARQL, Relational and Graph DatabasesProperty: http://www.w3.org/2002/07/owl#sameAshttp://data.semanticweb.org/conference/iswc/2016/paper/resource/resource-69Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/enzo-zeregahttps://w3id.org/scholarlydata/person/aidan-hoganhttps://w3id.org/scholarlydata/person/carlos-rojashttps://w3id.org/scholarlydata/person/daniel-hernandezhttps://w3id.org/scholarlydata/person/cristian-riverosProperty: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/cristian-riveroshttps://w3id.org/scholarlydata/person/daniel-hernandezhttps://w3id.org/scholarlydata/person/aidan-hoganhttps://w3id.org/scholarlydata/person/carlos-rojashttps://w3id.org/scholarlydata/person/enzo-zeregaProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractIn this paper, we experimentally compare the efficiency of various database engines for the purposes of querying the Wikidata knowledge-base, which can be conceptualised as a directed edge-labelled where edges can be annotated with meta-information called qualifiers. We select two popular SPARQL databases (Virtuoso, Blazegraph), a popular relational database (PostgreSQL), and a popular graph database (Neo4J) for comparison and discuss various options as to how Wikidata can be represented in the models of each engine. We design a set of experiments to test the relative query performance of these representations in the context of their respective engines. We first execute a large set of atomic lookups to establish a baseline performance for each test setting, and subsequently perform experiments on instances of more complex graph patterns based on real-world examples. We conclude with a summary of the strengths and limitations of the engines observed.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69/authorListProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2016/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordreificationwikidatagraph databasesrelational databasesproperty graphssparqlProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleQuerying Wikidata: Comparing SPARQL, Relational and Graph Databases
Subject: https://w3id.org/scholarlydata/person/aidan-hoganProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69
Subject: https://w3id.org/scholarlydata/person/carlos-rojasProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69
Subject: https://w3id.org/scholarlydata/person/cristian-riverosProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69
Subject: https://w3id.org/scholarlydata/person/daniel-hernandezProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69
Subject: https://w3id.org/scholarlydata/person/enzo-zeregaProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69
Subject: https://w3id.org/scholarlydata/conference/iswc/2016/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasParthttps://w3id.org/scholarlydata/inproceedings/iswc2016/paper/resource/resource-69