A combination of the SPARQL OPTIONAL keyword and the bound(...) filter function can be used to mimic some universally quantified queries. As an example, consider this query which finds the minimum price of every book in the underlying default graph: PREFIX ex: <http://example.org/> SELECT ?book ?minprice WHERE { ?book a ex:book ; ex:price ?minprice . OPTIONAL { ?book ex:price ?otherprice . FILTER( ?otherprice < ?minprice ) . } . FILTER ( !bound(?otherprice) ) .

Several software packages exist which allow SPARQL queries to generate answers from data sources other than RDF, such as relational databases, LDAP servers, or XML data. The community maintains a list of these tools at the W3C ESW Wiki.

It is sometimes desirable to use SPARQL to query triples entailed from subclass, subproperty, range, domain, and other relations which can be represented using RDF Schema. The SPARQL specification defines the results of queries based on RDF simple entailment. However, the specification does present a general, parametrized definition of graph pattern matching that can be expanded to other entailments beyond RDF simple entailment.

For the most part, the answer to this question is the same as the above answer regarding RDFS entailment. However, OWL-DL axioms do not always result in a unique deductive closure, and as such querying OWL-DL entailments requires instantiating the parametrized SPARQL basic graph pattern definition with values appropriate for OWL-DL entailment. The open-source OWL-DL reasoner, Pellet, will answer SPARQL queries while considering OWL-DL entailments.

SPARQL provides the function, regex(), which can be used to test whether a literal value contains a certain substring: SELECT ?title WHERE { _:book :title ?title . FILTER (regex(?title, "SPARQL")) . } The regex() function expects its first argument to be either a plain literal without a language tag or else a typed literal with a datatype of xsd:string. Plain literals with a language tag or typed literals of other datatypes will evaluate to a type error which causes the filter to fail.

While SPARQL does support nested graph patterns, it does not directly support subqueries (for example, the FROM clause of a SPARQL query cannot itself contain a CONSTRUCT query which generates the dataset to be queried against). The Data Access Working Group postponed this issue in early 2005.

For the most part, blank nodes in SPARQL queries function exactly as variables which cannot be returned to the user/client. So, the following two queries behave identically: SELECT ?title WHERE { _:book :hasTitle ?title } SELECT ?title WHERE { ?book :hasTitle ?title } However, a SPARQL query may not reuse the same blank node label twice in different basic graph patterns. That is, the following is not a legal SPARQL query: SELECT ?title WHERE { _:book rdfs:seeAlso ?g .

There is no built-in support within SPARQL to query hierarchical structures of an unknown depth (e.g. trees or lists), to query transitive relations, or to query via XPath like paths. The Data Access Working Group postponed this issue in early 2005. Repeated queries. A repeating structure can be queried via a series of queries.

Maximum £500,000.00 + vat (subject to the usual credit assessment procedures). Minimum £500.00 + vat.

SPARQL is a recursive acronym standing for SPARQL Protocol and RDF Query Language. As the name implies, SPARQL is a general term for both a protocol and a query language. Most uses of the SPARQL acronym refer to the RDF query language. In this usage, SPARQL is a syntactically-SQL-like language for querying RDF graphs via pattern matching. The language's features include basic conjunctive patterns, value filters, optional patterns, and pattern disjunction.

These terms define the smallest and largest numbers you want to see included in the problems on the work sheets being created.

The current, standardized version of SPARQL deals only with retrieving selected data from RDF graphs. There is no equivalent of the SQL INSERT, UPDATE, or DELETE statements. Most RDF-based applications handle new, changing, and stale data directly via the APIs provided by specific RDF storage systems. Alternatively, RDF data can exist virtually (i.e. created on-demand in response to a SPARQL query).

There is no minimum or maximum amount of properties or units needed for a Property Owner or Manager to use Tenantplus. In fact, our site is perfect for the property owner who manages a small number of units without the aid of a Property Management Company, or very large Property Management Companies who may have many properties with several units all over the country.

There are a variety of SPARQL tutorials and introductions scattered around the Web. Some notable ones include:

The difference between maximum and minimum fault values are caused by a number of factors. 1) The source impedance elements which are used at the supply point to your system let you specify maximum and minimum source impedances. The maximum source impedance applies to the minimum fault currents, and vice versa. 2) Maximum fault current levels are reported for the case where motors and generators are assumed attached to the system.

Effective July 1, 2007, the minimum is $44 per week. The maximum is $320 for claims filed on or after July 1, 2006. The amount an individual will receive is based on the amount of wages earned in the base period.

Maximum Concentration Values (MCVs) are limits set by the European Commission for each RoHS-restricted substance in the Commission Decision 2005/618/EC amending the RoHS Directive. MCV limits apply to each "homogeneous material" making up a product. Note that EU officials have stated that RoHS is considered to be a ban on the listed substances, and that any intentional use of those substances is not allowed.

The creators of several SPARQL implementations provide online services where SPARQL queries can be input and executed against either canned datasets or arbitrary datasets (identified by URLs). The community maintains a list of SPARQL endpoints on the W3C ESW Wiki.