@prefix ns0: . @prefix ns1: . ns1:iswc2008-paper-poster_demo-83-abstract-views-ebsco ns0:isIndicatorOf . ns1:iswc2008-paper-poster_demo-83-citation-count ns0:isIndicatorOf . ns1:iswc2008-paper-poster_demo-83-plumx-abstract-views ns0:isIndicatorOf . ns1:iswc2008-paper-poster_demo-83-plumx-captures ns0:isIndicatorOf . ns1:iswc2008-paper-poster_demo-83-plumx-readers ns0:isIndicatorOf . ns1:iswc2008-paper-poster_demo-83-plumx-usage ns0:isIndicatorOf . ns1:iswc2008-paper-poster_demo-83-readers-mendeley ns0:isIndicatorOf . @prefix foaf: . @prefix ns3: . ns3:jens-lehmann foaf:made . ns3:sebastian-hellmann foaf:made . ns3:soeren-auer foaf:made . @prefix ns4: . @prefix ns5: . ns5:poster_demo_proceedings ns4:hasPart . @prefix rdf: . @prefix ns7: . rdf:type ns7:SocialObject , ns4:InProceedings , ns7:InformationObject , ns7:Object . @prefix rdfs: . rdf:type rdfs:Resource . @prefix owl: . rdf:type owl:Thing . @prefix ns10: . rdf:type ns10:ProceedingsPaper ; rdfs:label "Learning of OWL Class Descriptions on Very Large Knowledge Bases" ; owl:sameAs ; ns0:hasIndicator ns1:iswc2008-paper-poster_demo-83-abstract-views-ebsco , ns1:iswc2008-paper-poster_demo-83-citation-count , ns1:iswc2008-paper-poster_demo-83-plumx-abstract-views , ns1:iswc2008-paper-poster_demo-83-plumx-captures , ns1:iswc2008-paper-poster_demo-83-plumx-readers , ns1:iswc2008-paper-poster_demo-83-plumx-usage , ns1:iswc2008-paper-poster_demo-83-readers-mendeley . @prefix ns11: . ns11:abstract "The vision of the Semantic Web is to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are emerging and are freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, we present an approach for obtaining complex class descriptions from objects in knowledge bases by using Machine Learning techniques. We describe how we leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. Our algorithms are made available in the open source DL-Learner project and can be used in real-life scenarios by Semantic Web applications." . @prefix dc: . dc:creator ns3:jens-lehmann , ns3:soeren-auer , ns3:sebastian-hellmann ; dc:subject "Large Knowledge Bases" , "Machine Learning" , "SPARQL" , "OWL" , "Class Description" ; dc:title "Learning of OWL Class Descriptions on Very Large Knowledge Bases" . @prefix ns13: . @prefix ns14: . ns13:authorList ns14:authorList ; foaf:maker ns3:sebastian-hellmann , ns3:soeren-auer , ns3:jens-lehmann ; ns4:abstract "The vision of the Semantic Web is to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are emerging and are freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, we present an approach for obtaining complex class descriptions from objects in knowledge bases by using Machine Learning techniques. We describe how we leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. Our algorithms are made available in the open source DL-Learner project and can be used in real-life scenarios by Semantic Web applications." ; ns4:doi "10.4018/jswis.2009040102" ; ns4:hasAuthorList ns14:authorList ; ns4:isPartOf ns5:poster_demo_proceedings . @prefix xsd: . ns4:keyword "OWL"^^xsd:string , "Machine Learning"^^xsd:string , "SPARQL"^^xsd:string , "Large Knowledge Bases"^^xsd:string , "Class Description"^^xsd:string ; ns4:title "Learning of OWL Class Descriptions on Very Large Knowledge Bases" .