SPARQL (pronounced "sparkle", a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language—that is, a semantic query language for databases—able to retrieve and manipulate data stored in Resource Description Framework (RDF) 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 was acknowledged by W3C as an official 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. There exist tools that allow one to connect and semi-automatically construct a SPARQL query for a SPARQL endpoint, for example ViziQuer. In addition, tools exist to translate SPARQL queries to other query languages, for example to SQL and to XQuery.

Advantages

SPARQL allows users to write queries that follow the RDF specification of the W3C. Thus, the entire dataset is "subject-predicate-object" triples. Subjects and predicates are always URI identifiers, but objects can be URIs or literal values. This single physical schema of 3 "columns" is hyperdenormalized in that what would be 1 relational record with 4 fields is now 4 triples with the subject being repeated over and over, the predicate essentially being the column name, and the object being the field value. Although this seems unwieldy, the SPARQL syntax offers these features: 1. Subjects and Objects can be used to find the other including recursively. Below is a set of triples. It should be clear that and link to , which itself has links: In SPARQL, the first time a variable is encountered in the expression pipeline, it is populated with result. The second and subsequent times it is seen, it is used as an input. If we assign ("bind") the URI to the variable, then it drives a result into ; this tells us all the things that link to (upstream dependency): But with a simple switch of the binding variable, the behavior is reversed. This will produce all the things upon which depends (downstream dependency): Even more attractive is that we can easily instruct SPARQL to recursively follow the path: Bound variables can therefore also be lists and will be operated upon without complicated syntax. The effect of this is similar to the following: 2. SPARQL expressions are a pipeline Unlike SQL which has subqueries and CTEs, SPARQL is much more like MongoDB or SPARK. Expressions are evaluated exactly in the order they are declared including filtering and joining of data. The programming model becomes what a SQL statement would be like with multiple WHERE clauses. The combination of list-aware subjects and objects plus a pipeline approach can yield extremely expressive queries spanning many different domains of data. Unlike relational databases, the object column is heterogeneous: the object data type, if not an URI, is usually implied (or specified in the ontology) by the predicate value. Literal nodes carry type information consistent with the underlying XSD namespace including signed and unsigned short and long integers, single and double precision floats, datetime, penny-precise decimal, Boolean, and string. Triple store implementations on traditional relational databases will typically store the value as a string and a fourth column will identify the real type. Polymorphic databases such as MongoDB and SQLite can store the native value directly into the object field. Thus, SPARQL provides a full set of analytic query operations such as, , for data whose schema is intrinsically part of the data rather than requiring a separate schema definition. However, schema information (the ontology) is often provided externally, to allow joining of different datasets unambiguously. In addition, SPARQL provides specific graph traversal syntax for data that can be thought of as a graph. The example below demonstrates a simple query that leverages the ontology definition ("friend of a friend"). Specifically, the following query returns names and emails of every person in the dataset: This query joins all of the triples with a matching subject, where the type predicate, " ", is a person, and the person has one or more names and mailboxes. For the sake of readability, the author of this query chose to reference the subject using the variable name " ". Since the first element of the triple is always the subject, the author could have just as easily used any variable name, such as " " or " ". Whatever name is chosen, it must be the 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 –, ,. This query returns the and because is often a complex URI rather than a human-friendly string. Note that any may have multiple mailboxes, so in the returned set, a 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 fuel efficiency.

Query forms

In the case of queries that read data from the database, the SPARQL language specifies four different query variations for different purposes. Each of these query forms takes a block to restrict the query, although, in the case of the query, the 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?": Variables are indicated by a or prefix. Bindings for and the will be returned. When a triple ends with a semicolon, the subject from this triple will implicitly complete the following pair to an entire triple. So for example is short for. 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 " " stands for “ ”.

Extensions

GeoSPARQL defines filter functions for geographic information system (GIS) queries using well-understood OGC standards (GML, WKT, etc.). SPARUL is another extension to SPARQL. It enables the RDF store to be updated with this declarative query language, by adding and methods. XSPARQL is an integrated query language combining XQuery with SPARQL to query both XML and RDF data sources at once.

Implementations

Open source, reference SPARQL implementations See List of SPARQL implementations for more comprehensive coverage, including triplestore, APIs, and other storages that have implemented the SPARQL standard.