In this paper, we experimentally compare the efficiency of
various database engines for the purposes of querying the Wikidata
knowledge-base, which can be conceptualised as a directed edge-labelled
graph where edges can be annotated with meta-information called quali-
fiers. We take two popular SPARQL databases (Virtuoso, Blazegraph), a
popular relational database (PostgreSQL), and a popular graph database
(Neo4J) for comparison and discuss various options as to how Wikidata
can be represented in the models of each engine. We design a set of
experiments to test the relative query performance of these representa-
tions in the context of their respective engines. We first execute a large
set of atomic lookups to establish a baseline performance for each test
setting, and subsequently perform experiments on instances of more com-
plex graph patterns based on real-world examples. We conclude with a
summary of the strengths and limitations of the engines observed.
%0 Generic
%1 hernandez2016querying
%A Hernández, Daniel
%A Hogan, Aidan
%A Riveros, Cristian
%A Rojas, Carlos
%A Zerega, Enzo
%D 2016
%K blazegraph comparison database graph neo4j paper query sparql virtuoso wikidata
%T Querying Wikidata: Comparing SPARQL, Relational and Graph Databases
%U http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf
%X In this paper, we experimentally compare the efficiency of
various database engines for the purposes of querying the Wikidata
knowledge-base, which can be conceptualised as a directed edge-labelled
graph where edges can be annotated with meta-information called quali-
fiers. We take two popular SPARQL databases (Virtuoso, Blazegraph), a
popular relational database (PostgreSQL), and a popular graph database
(Neo4J) for comparison and discuss various options as to how Wikidata
can be represented in the models of each engine. We design a set of
experiments to test the relative query performance of these representa-
tions in the context of their respective engines. We first execute a large
set of atomic lookups to establish a baseline performance for each test
setting, and subsequently perform experiments on instances of more com-
plex graph patterns based on real-world examples. We conclude with a
summary of the strengths and limitations of the engines observed.
@preprint{hernandez2016querying,
abstract = {In this paper, we experimentally compare the efficiency of
various database engines for the purposes of querying the Wikidata
knowledge-base, which can be conceptualised as a directed edge-labelled
graph where edges can be annotated with meta-information called quali-
fiers. We take two popular SPARQL databases (Virtuoso, Blazegraph), a
popular relational database (PostgreSQL), and a popular graph database
(Neo4J) for comparison and discuss various options as to how Wikidata
can be represented in the models of each engine. We design a set of
experiments to test the relative query performance of these representa-
tions in the context of their respective engines. We first execute a large
set of atomic lookups to establish a baseline performance for each test
setting, and subsequently perform experiments on instances of more com-
plex graph patterns based on real-world examples. We conclude with a
summary of the strengths and limitations of the engines observed.},
added-at = {2016-09-21T18:36:47.000+0200},
author = {Hernández, Daniel and Hogan, Aidan and Riveros, Cristian and Rojas, Carlos and Zerega, Enzo},
biburl = {https://www.bibsonomy.org/bibtex/2a595e75656f6757031e670652c601e90/brightbyte},
interhash = {f892442e8e92d1b71991a8d0bae4cad5},
intrahash = {a595e75656f6757031e670652c601e90},
keywords = {blazegraph comparison database graph neo4j paper query sparql virtuoso wikidata},
timestamp = {2016-09-21T18:38:11.000+0200},
title = {Querying Wikidata: Comparing SPARQL, Relational and Graph Databases},
url = {http://aidanhogan.com/docs/wikidata-sparql-relational-graph.pdf},
year = 2016
}