Also from ESWC 2023 I found interesting, the Finnish Sampo project; it is using Fuseki in the back-end: https://seco.cs.aalto.fi/publications/2023/hyvonen-et-al-ps-data-2023.pdf
Their demo: https://parlamenttisampo.fi/ On Wed, 2023-05-31 at 10:12 +0200, LB wrote: > Hi all, > > > slightly off-topic, but given the ongoing ESWC 2023 conference, I > want > to share two papers that might be interesting for the one or the > other: > > 1. Join Ordering of SPARQL Property Path Queries > > > SPARQL property path queries provide a succinct way to write > > complex > > navigational queries over RDF knowledge graphs. However, their > > evaluation remains difficult as they may involve the execution of > > transitive closures. As a result, many property path queries just > > timeout when executed on public online RDF knowledge graphs. One > > solution to speed up their execution is to find optimal join > > orders. > > Although the join ordering problem has been extensively studied for > > traditional SPARQL queries, the presence of property path patterns > > biases existing approaches. In this paper we focus on C2RP QUF > > queries > > (conjunctive SPARQL property path queries with UNION and FILTER), > > and > > we present a query optimizer that is able to capture the cost of > > C2RP > > QUF queries using an appropriate cost model and a sampling-based > > cardinality estimator. On the latest Wikidata Query Benchmark, we > > empirically demonstrate that our approach finds significantly > > better > > join orders than Virtuoso and BlazeGraph. > > Paper: > https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Aimonier-Davat_2023_Join.pdf > > Not directly related to Jena, but interesting anyways. > > > 2. Evaluation of a Representative Selection of SPARQL Query Engines > using Wikidata > > > In this paper, we present an evaluation of the performance of five > > representative RDF triplestores, including GraphDB, Jena Fuseki, > > Neptune, RDFox, and Stardog, and one experimental SPARQL query > > engine, > > QLever. We compare importing time, loading time, and exporting time > > using a complete version of the knowledge graph Wikidata, and we > > also > > evaluate query performances using 328 queries defined by Wikidata > > users. To put this evaluation into context with respect to previous > > evaluations, we also analyze the query performances of these > > systems > > using a prominent synthetic benchmark: SP2Bench. We observed that > > most > > of the systems we considered for the evaluation were able to > > complete > > the execution of almost all the queries defined by Wikidata users > > before the timeout we established. We noticed, however, that the > > time > > needed by most systems to import and export Wikidata might be > > longer > > than required in some industrial and academic projects, where > > information is represented, enriched, and stored using different > > representation means. > > Paper: > https://2023.eswc-conferences.org/wp-content/uploads/2023/05/paper_Lam_2023_Evaluation.pdf > > In the second paper Jena TDB2 (v4.4.0) has been used during the > benchmark. > > > Cheers, > > Lorenz > >
