Querying Large Knowledge Graphs over Triple Pattern Fragments: An Empirical Study
Publication date
2018-09-18
Document type
Konferenzbeitrag
Author
Organisational unit
Karlsruhe Institute of Technology
ISBN
ISSN
Conference
17th International Semantic Web Conference (ISWC 2018) : Monterey, CA, USA, October 8-12, 2018
Series or journal
Lecture Notes in Computer Science
Periodical volume
11137
Book title
The Semantic Web - ISWC 2018 : 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part II
First page
86
Last page
102
Part of the university bibliography
Nein
Abstract
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 https://doi.org/10.5281/zenodo.1211621 Titel anhand dieser DOI in Citavi-Projekt übernehmen. 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. © Springer Nature Switzerland AG 2018.
Cite as
Heling, L., Acosta, M., Maleshkova, M., Sure-Vetter, Y. (2018). Querying Large Knowledge Graphs over Triple Pattern Fragments: An Empirical Study. In: Vrandečić, D., et al. The Semantic Web – ISWC 2018. ISWC 2018. Lecture Notes in Computer Science(), vol 11137. Springer, Cham. https://doi.org/10.1007/978-3-030-00668-6_6
Version
Published version
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