@prefix foaf: . @prefix ns1: . @prefix ns2: . ns1:maria-maleshkova foaf:made ns2:iswc-2018-resources-215 . ns1:maribel-acosta foaf:made ns2:iswc-2018-resources-215 . ns1:york-sure-vetter foaf:made ns2:iswc-2018-resources-215 . @prefix ns3: . @prefix ns4: . ns4:proceedings ns3:hasPart ns2:iswc-2018-resources-215 . @prefix rdf: . ns2:iswc-2018-resources-215 rdf:type ns3:InProceedings . @prefix rdfs: . ns2:iswc-2018-resources-215 rdfs:label "Querying Large Knowledge Graphs over Triple Pattern Fragments: An Empirical Study" . @prefix dc: . ns2:iswc-2018-resources-215 dc:creator ns1:york-sure-vetter , ns1:maribel-acosta , ns1:maria-maleshkova , ns1:lars-heling ; ns3:abstract "Triple Pattern Fragments (TPFs) are a novel interface for accessing data in knowledge graphs on the web. Up to this date, work on performance evaluation and optimization has focused mainly on SPARQL query execution over TPF servers. However, in order to devise querying techniques that efficiently access large knowledge graphs via TPFs, we need to identify and understand the variables that influence the performance of TPF servers on a fine-grained level. \nIn this work, we assess the performance of TPFs by measuring the response time for different requests and analyze how the requests' properties, as well as the TPF server configuration, may impact the performance. For this purpose, we developed the emph{Triple Pattern Fragment Profiler} to determine the performance of TPF server. The resource is openly available at url{https://github.com/Lars-H/tpf_profiler}.\nTo this end, we conduct an empirical study over four real-world knowledge graphs in different server environments and configurations. As part of our analysis, we provide an extensive evaluation of the results and focus on the impact of the variables: triple pattern type, answer cardinality, page size, backend and the environment type on the response time. The results suggest that all variables impact on the measured response time and allow for deriving suggestions for TPF server configurations and query optimization." . @prefix ns8: . ns2:iswc-2018-resources-215 ns3:hasAuthorList ns8:iswc-2018-resources-215 ; ns3:isPartOf ns4:proceedings . @prefix xsd: . ns2:iswc-2018-resources-215 ns3:keyword "SPARQL"^^xsd:string , "Querying"^^xsd:string , "Triple Pattern Fragment"^^xsd:string , "Linked Data"^^xsd:string , "Empirical Study"^^xsd:string ; ns3:title "Querying Large Knowledge Graphs over Triple Pattern Fragments: An Empirical Study" . ns1:lars-heling foaf:made ns2:iswc-2018-resources-215 . @prefix ns10: . ns10:iswc2018-author-lars-heling-iswc-2018-resources-215 ns3:withDocument ns2:iswc-2018-resources-215 . ns10:iswc2018-author-maria-maleshkova-iswc-2018-resources-215 ns3:withDocument ns2:iswc-2018-resources-215 . ns10:iswc2018-author-maribel-acosta-iswc-2018-resources-215 ns3:withDocument ns2:iswc-2018-resources-215 . ns10:iswc2018-author-york-sure-vetter-iswc-2018-resources-215 ns3:withDocument ns2:iswc-2018-resources-215 .