Querying Large Knowledge Graphs over Triple Pattern Fragments: An Empirical Study

  • Lars HelingEmail author
  • Maribel Acosta
  • Maria Maleshkova
  • York Sure-Vetter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11137)


Triple Pattern Fragments (TPFs) are a novel interface for accessing data in knowledge graphs on the web. So far, 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. In 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 Triple Pattern Fragment Profiler to determine the performance of TPF server. The resource is openly available at To this end, we conduct an empirical study over four large 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.



The authors thank Ruben Verborgh for providing feedback and the KG dumps and Javier Fernández for the fruitful discussions about HDT. This work was carried out with the support of the German Research Foundation (DFG) within the project “Sozial-Raumwissenschaftliche Forschungsdateninfrastruktur (SoRa)”.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lars Heling
    • 1
    Email author
  • Maribel Acosta
    • 1
  • Maria Maleshkova
    • 1
  • York Sure-Vetter
    • 1
  1. 1.Institute AIFBKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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