Database Reference
In-Depth Information
Figure 1. Sample RDF graph
Figure 2. Sample SPARQL query
The SPARQL query language is the official W3C standard for querying and extracting information
from RDF graphs (Prud'hommeaux & Seaborne, 2008). RDF is a directed labeled graph data format
and, thus, SPARQL is essentially a graph-matching query language. It represents the counterpart to
select-project-join
queries in the relational model. It is based on a powerful graph matching facility,
allows binding variables to components in the input RDF graph and supports conjunctions and disjunc-
tions of triple patterns. In addition, operators akin to relational joins, unions, left outer joins, selections,
and projections can be combined to build more expressive queries. A basic SPARQL query has the form:
select ?variable1 ?variable2...
where { pattern1. pattern2.... }
Figure 3 illustrates a general classification for RDF tripe stores based on their storage models Ma et
al. (2008). In principle, RDF stores can be divided into two major categories:
native
stores and
database-
based
stores. Native stores are directly built on the file system, whereas database based repositories use
relational or object relational databases as the backend store. Representative native stores include
OWLIM (Kiryakov et al., 2005), HStar (Ma et al., 2008), AllegroGraph (
AllegroGraph RDFStore
, 2009)
and YARs (Harth & Decker, 2005). Representative of ontology-dependent stores include DLDB (Pan
& Heflin, 2003; Pan et al., 2008) and Sesame (Broekstra et al., 2002). The main focus of this chapter is
to give an overview of the
generic relational
approaches for processing RDF data.
In general, relational database management systems (RDBMSs) have repeatedly shown that they are
very efficient, scalable and successful in hosting types of data which have formerly not been antici-
pated to be stored inside relational databases. In addition, RDBMSs have shown its ability to handle
