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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 became an official W3C Recommendation,〔(【引用サイトリンク】title=XML and Semantic Web W3C Standards Timeline )〕 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, there exist tools that translate SPARQL queries to other query languages, for example to SQL〔(【引用サイトリンク】title= D2R Server )〕 and to XQuery.〔(【引用サイトリンク】title= SPARQL2XQuery Framework )〕 == Advantages == SPARQL allows users to write queries against what 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 relations, 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") for the same key, 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 and fig. 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: SELECT ?name ?email WHERE 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 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 – ?person, ?name, ?email. This query returns the ?name and ?email because ?person is often a complex URI rather than a human-friendly string. Note that 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 fuel efficiency. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「SPARQL」の詳細全文を読む スポンサード リンク
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