SPARQL

SPARQL

Paradigm	Query language
Developer	W3C
First appeared	2008
Stable release	1.1 / 2013-03-21
Website	[1]
Major implementations
Jena, OpenLink Virtuoso

SPARQL (pronounced "sparkle", an acronym for Simple Protocol and RDF Query Language) is an RDF query language, that is, a query language for databases, able to retrieve and manipulate data stored in Resource Description Framework format. It was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium, and is recognized as one of the key technologies of the semantic web. On 15 January 2008, SPARQL 1.0 became an official W3C Recommendation, and SPARQL 1.1 in March, 2013.

SPARQL allows for a query to consist of triple patterns, conjunctions, disjunctions, and optional patterns.

Implementations for multiple programming languages exist. "SPARQL will make a huge difference" making the web machine-readable according to Sir Tim Berners-Lee in a May 2006 interview.

There exist tools that allow one to connect and semi-automatically construct a SPARQL query for a SPARQL endpoint, for example ViziQuer. In addition, there exist tools that translate SPARQL queries to other query languages, for example to SQL and to XQuery. SPARQL City's SPARQLverse also allows queries directly against non-SPARQL databases such as MongoDB and Cassandra, representing their data as though it is RDF.

Advantages

SPARQL allows users to write queries against data that can loosely be called "key-value" data or, more specifically, data that follows the RDF specification of the W3C. The entire database is thus a set of "subject-predicate-object" triples. This is analogous to some NoSQL databases' usage of the term "document-key-value", such as MongoDB.

RDF data can also be considered in SQL relational database terms as a table with three columns - the subject column, the predicate column and the object column. Unlike relational databases, the object column is heterogeneous, the per-cell data type is usually implied (or specified in the ontology) by the predicate value. Alternately, again comparing to SQL relational, all of the triples for a given subject could be represented as a row, with the subject being the primary key and each possible predicate being a column and the object is the value in the cell. However, SPARQL/RDF becomes easier and more powerful for columns that could contain multiple values (like "children"), and where the column itself could be a joinable variable in the query, rather than directly specified.

SPARQL thus provides a full set of analytic query operations such as JOIN, SORT, AGGREGATE for data whose schema is intrinsically part of the data rather than requiring a separate schema definition. Schema information (the ontology) is often provided externally, though, to allow different datasets to be joined in an unambiguous manner. In addition, SPARQL provides specific graph traversal syntax for data that can be thought of as a graph. Some implementations, such as SPARQLverse also allow additional triple attributes such as timestamp and allow additional analytic functionality such as windowed aggregates.

The example below demonstrates a simple query that leverages the ontology definition "foaf", often called the "friend-of-a-friend" ontology.

Specifically, the following query returns names and emails of every person in the dataset:

PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name ?email
WHERE {
  ?person a foaf:Person.
  ?person foaf:name ?name.
  ?person foaf:mbox ?email.
}

This query joins together all of the triples with a matching subject, where the type predicate, "a", is a person (foaf:Person) and the person has one or more names (foaf:name) and mailboxes (foaf:mbox).

The author of this query chose to reference the subject using the variable name "?person" for readable clarity. Since the first element of the triple is always the subject, the author could have just as easily used any variable name, such as "?subj" or "?x". Whatever name is chosen, it must be that same on each line of the query to signify that the query engine is to join triples with the same subject.

The result of the join is a set of rows - ?person, ?name, ?email. This query is returning the ?name and ?email because ?person is often a complex URI rather than a human-friendly string. Note that in some of the ?people may have multiple mailboxes, so in the returned set, a ?name row may appear multiple times, once for each mailbox.

This query can be distributed to multiple SPARQL endpoints (services that accept SPARQL queries and return results), computed, and results gathered, a procedure known as federated query.

Whether in a federated manner or locally, additional triple definitions in the query could allow joins to different subject types, such as automobiles, to allow simple queries, for example, to return a list of names and emails for people who drive automobiles with a high MPG rating.

Query forms

In the case of queries that read data from the database, the SPARQL language specifies four different query variations for different purposes.

SELECT query

Used to extract raw values from a SPARQL endpoint, the results are returned in a table format.

CONSTRUCT query

Used to extract information from the SPARQL endpoint and transform the results into valid RDF.

ASK query

Used to provide a simple True/False result for a query on a SPARQL endpoint.

DESCRIBE query

Used to extract an RDF graph from the SPARQL endpoint, the contents of which is left to the endpoint to decide based on what the maintainer deems as useful information.

Each of these query forms takes a WHERE block to restrict the query although in the case of the DESCRIBE query the WHERE is optional.

SPARQL 1.1 specifies a language for updating the database with several new query forms.

Example

Another SPARQL query example that models the question "What are all the country capitals in Africa?":

PREFIX abc: <http://example.com/exampleOntology#>
SELECT ?capital ?country
WHERE {
  ?x abc:cityname ?capital ;
     abc:isCapitalOf ?y .
  ?y abc:countryname ?country ;
     abc:isInContinent abc:Africa .
}

Variables are indicated by a "?" or "$" prefix. Bindings for ?capital and the ?country will be returned.

The SPARQL query processor will search for sets of triples that match these four triple patterns, binding the variables in the query to the corresponding parts of each triple. Important to note here is the "property orientation" (class matches can be conducted solely through class-attributes or properties - see Duck typing)

To make queries concise, SPARQL allows the definition of prefixes and base URIs in a fashion similar to Turtle. In this query, the prefix "abc" stands for “http://example.com/exampleOntology#”.

Extensions

GeoSPARQL defines filter functions for geographic information system (GIS) queries using well-understood OGC standards (GML, WKT, etc.).

SPARQL implementations

This list shows triplestore, APIs, and other storages that have implemented the SPARQL query language.

• 4store
• Algebraix Data SPARQL Server
• AllegroGraph
• Apache Jena with ARQ
• ARC2
• BigData
• BrightstarDB
• Corese
• D2R Server
• Dydra
• Hercules
• Intellidimension Semantics Platform 2.0
• KAON2
• Knowledge Explorer
• LUPOSDATE
• MarkLogic
• Mulgara
• OntoBroker
• Ontotext OWLIM
• Open Anzo
• OpenLink Virtuoso
• Oracle Oracle DB Enterprise Spatial & Graph
• Oracle NoSQL KV RDF
• Parliament

• Pellet
• Profium Sense
• RAP RDF API for PHP
• RDF-3X
• rdfQuery JavaScript module
• RDF::Query Perl module with complete SPARQL 1.1 implementation.
• Redland / Redstore
• SemWeb.NET
• Sesame 2
• SPARQL Engine
• SPARQL2XQuery
• SPARQL-RW
• SPARQL City
• Stardog
• SWObjects
• TopQuadrant's TopBraid Suite
• Twinql
• Ultrawrap
• Web Query
• IBM DB2

External links

• W3C SPARQL Working Group, was RDF Data Access Working Group
• SPARQL 1.1 Recommendation
• SPARQL 1.0 Query language (legacy)
• SPARQL 1.0 Protocol (legacy)
• SPARQL 1.0 Query XML Results Format (legacy)