DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data

By Mohamed Morsey, Jens Lehmann, Sören Auer, Axel-Cyrille Ngonga Ngomo. International Workshop on the Semantic Web, 2011

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c4e299be0c1aa7e1363bf57814c5e27d04a0b874

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ihab F. Ilyas
2. Xu Chu
3. Paolo Papotti
4. Nan Tang
5. Mourad Ouzzani
6. Olaf Hartig
7. M. Tamer Özsu
8. Ruben Verborgh

In Computer Networks and Communications

1. Xu Chu
2. Ihab F. Ilyas
3. Paolo Papotti
4. Nan Tang
5. Olaf Hartig
6. M. Tamer Özsu
7. Ruben Verborgh
8. Mourad Ouzzani

In Management Science and Operations Research

1. Ihab F. Ilyas
2. Xu Chu
3. Paolo Papotti
4. Nan Tang
5. Mourad Ouzzani
6. M. Tamer Özsu
7. Sanjay Krishnan
8. Eugene Wu

Citations in the corpus

• Diversified Stress Testing of RDF Data Management Systems (2014, methodology): “– The DBpedia SPARQL Benchmark [17] (DBSB) uses queries that have been generated by mining actual query logs over the DBpedia dataset [5].”
• KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, background): “We used Yago [21] and DBpedia [27] as the underlying kb s .”
• Triple Pattern Fragments: A low-cost knowledge graph interface for the Web (2016, methodology): “The benchmark incorporates queries from the public DBpedia SPARQL endpoint log, filtering nonrelevant variations and queries with a low frequency [57].”