Paper 29 (Research track)

The Cost of Querying Knowledge Graphs over Triple Pattern Fragments: An Empirical Study

Author(s): Lars Heling, Maribel Acosta, Maria Maleshkova, York Sure-Vetter

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 the SPARQL query execution on top of TPF servers. However, in order to devise querying techniques that efficiently access 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 measure the performance of TPFs by measuring the response time for different requests, and analyze how the requests’ properties may impact the performance. To this end, we conduct an empirical study over four knowledge graphs in different server environments. As part of our anal- ysis, we provide an extensive evaluation of the results and focus on the impact of the variables: triple pattern type, answer cardinality, caching and the environment type on the response time. The observed results suggest that all variables have an impact on the measured response time.

Keywords: Linked Data; Triple Pattern Fragment; Empirical Study; Querying; SPARQL

Leave a Reply

Your email address will not be published. Required fields are marked *