@prefix foaf: . @prefix ns1: . @prefix ns2: . ns1:jacopo-urbani foaf:made ns2:iswc-2017-iswc-2017-posters-and-demos-646 . ns1:ceriel-jacobs foaf:made ns2:iswc-2017-iswc-2017-posters-and-demos-646 . @prefix ns3: . @prefix ns4: . ns4:proceedings ns3:hasPart ns2:iswc-2017-iswc-2017-posters-and-demos-646 . @prefix rdf: . @prefix rdfs: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 rdf:type rdfs:Resource . @prefix ns7: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 rdf:type ns7:InformationObject , ns7:Object , ns3:InProceedings , ns7:SocialObject . @prefix ns8: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 rdf:type ns8:ProceedingsPaper . @prefix owl: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 rdf:type owl:Thing ; rdfs:label "Predicting the Cost of Online Reasoning on Knowledge Graphs: Some Heuristics" . @prefix ns10: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 owl:sameAs ns10:iswc-2017-iswc-2017-posters-and-demos-646 . @prefix ns11: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 ns11:abstract "We study the problem of efficiently performing online reasoning, i.e.,computing all relevant implicit answers during query answering. We focus on reasoning that can be encoded with Datalog programs.Working with implementation of two query-driven Datalog evaluations: Query-Subquery (QSQ), a top-down procedure, and Magic-Set(MS),which proceeds bottom-up, one is preferable to the other depending on the amount of reasoning triggered by the query. Thus, to achieve the highest efficiency, we study how we can estimate whether a given query will be faster with QSQ or MS. We propose a number of heuristics for making such an estimate and evaluate them. Our experiments, conducted on various KGs and using the VLog engine, showed that individual estimators were able to choose the right algorithm in most of the cases. This increases significantly the efficiency of reasoning at query-time." . @prefix dc: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 dc:creator ns1:ceriel-jacobs , ns1:jacopo-urbani , ns1:varsha-ravichandra-mouli , ns1:unmesh-joshi . @prefix xsd: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 dc:subject "Magic set (MS)"^^xsd:string , "Datalog"^^xsd:string , "Query-subQuery(QSQ)"^^xsd:string , "VLog"^^xsd:string , "Online reasoning"^^xsd:string ; dc:title "Predicting the Cost of Online Reasoning on Knowledge Graphs: Some Heuristics" . @prefix ns14: . @prefix ns15: . ns2:iswc-2017-iswc-2017-posters-and-demos-646 ns14:authorList ns15:iswc-2017-iswc-2017-posters-and-demos-646 ; foaf:maker ns1:varsha-ravichandra-mouli , ns1:unmesh-joshi , ns1:ceriel-jacobs , ns1:jacopo-urbani ; ns3:abstract "We study the problem of efficiently performing online reasoning, i.e.,computing all relevant implicit answers during query answering. We focus on reasoning that can be encoded with Datalog programs.Working with implementation of two query-driven Datalog evaluations: Query-Subquery (QSQ), a top-down procedure, and Magic-Set(MS),which proceeds bottom-up, one is preferable to the other depending on the amount of reasoning triggered by the query. Thus, to achieve the highest efficiency, we study how we can estimate whether a given query will be faster with QSQ or MS. We propose a number of heuristics for making such an estimate and evaluate them. Our experiments, conducted on various KGs and using the VLog engine, showed that individual estimators were able to choose the right algorithm in most of the cases. This increases significantly the efficiency of reasoning at query-time." ; ns3:hasAuthorList ns15:iswc-2017-iswc-2017-posters-and-demos-646 ; ns3:isPartOf ns4:proceedings ; ns3:keyword "VLog"^^xsd:string , "Magic set (MS)"^^xsd:string , "Query-subQuery(QSQ)"^^xsd:string , "Online reasoning"^^xsd:string , "Datalog"^^xsd:string ; ns3:title "Predicting the Cost of Online Reasoning on Knowledge Graphs: Some Heuristics" . ns1:unmesh-joshi foaf:made ns2:iswc-2017-iswc-2017-posters-and-demos-646 . ns1:varsha-ravichandra-mouli foaf:made ns2:iswc-2017-iswc-2017-posters-and-demos-646 .