About https://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84

Subject: https://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingshttp://www.w3.org/2000/01/rdf-schema#Resourcehttp://www.w3.org/2002/07/owl#Thinghttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#InformationObjecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#Objecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjecthttp://purl.org/spar/fabio/ProceedingsPaperProperty: http://www.w3.org/2000/01/rdf-schema#labelA Scalable Approach for Efficiently Generating Structured Dataset Topic ProfilesProperty: http://www.w3.org/2002/07/owl#sameAshttp://data.semanticweb.org/conference/eswc/2014/paper/research/84Property: http://swrc.ontoware.org/ontology#abstractThe increasing adoption of Linked Data principles has led to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset profiles. A profile consists of structured dataset metadata describing topics and their relevance. Profiles are generated through the configuration of techniques for resource sampling from datasets, topic extraction from knowledge bases and their ranking based on graphical models. To enable a good trade-off between scalability and representatives of generated data, appropriate parameters are determined experimentally. Our evaluation considers topic profiles of all accessible datasets from the Linked Open Data cloud and shows that our approach generates representative profiles even with comparably small sample sizes (10%) outperforms established topic modelling approaches.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/wolfgang-nejdlhttps://w3id.org/scholarlydata/person/davide-taibihttps://w3id.org/scholarlydata/person/bernardo-pereira-nuneshttps://w3id.org/scholarlydata/person/besnik-fetahuhttps://w3id.org/scholarlydata/person/marco-antonio-casanovahttps://w3id.org/scholarlydata/person/stefan-dietzeProperty: http://purl.org/dc/elements/1.1/subjectLinked DataMetadataProfilingVocabulary of LinksProperty: http://purl.org/dc/elements/1.1/titleA Scalable Approach for Efficiently Generating Structured Dataset Topic ProfilesProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84/authorListProperty: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/wolfgang-nejdlhttps://w3id.org/scholarlydata/person/stefan-dietzehttps://w3id.org/scholarlydata/person/bernardo-pereira-nuneshttps://w3id.org/scholarlydata/person/besnik-fetahuhttps://w3id.org/scholarlydata/person/marco-antonio-casanovahttps://w3id.org/scholarlydata/person/davide-taibiProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractThe increasing adoption of Linked Data principles has led to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset profiles. A profile consists of structured dataset metadata describing topics and their relevance. Profiles are generated through the configuration of techniques for resource sampling from datasets, topic extraction from knowledge bases and their ranking based on graphical models. To enable a good trade-off between scalability and representatives of generated data, appropriate parameters are determined experimentally. Our evaluation considers topic profiles of all accessible datasets from the Linked Open Data cloud and shows that our approach generates representative profiles even with comparably small sample sizes (10%) outperforms established topic modelling approaches.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84/authorListProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/eswc/2014/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordProfilingVocabulary of LinksMetadataLinked DataProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleA Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles
Subject: https://w3id.org/scholarlydata/person/wolfgang-nejdlProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84
Subject: https://w3id.org/scholarlydata/person/stefan-dietzeProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84
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Subject: https://w3id.org/scholarlydata/person/bernardo-pereira-nunesProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84
Subject: https://w3id.org/scholarlydata/person/besnik-fetahuProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84
Subject: https://w3id.org/scholarlydata/person/davide-taibiProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84
Subject: https://w3id.org/scholarlydata/person/marco-antonio-casanovaProperty: http://xmlns.com/foaf/0.1/madehttps://w3id.org/scholarlydata/inproceedings/eswc2014/paper/research/84