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SPARQL By Example:
The Cheat Sheet
Accompanies slides at:
http://www.cambridgesemantics.com/semantic-university/sparql-by-example
Comments & questions to:
Lee Feigenbaum <lee@cambridgesemantics.com>
VP Marketing & Technology, Cambridge Semantics
Co-chair, W3C SPARQL Working Group
Conventions
Red text means:
“This is a core part of the SPARQL syntax or
language.”
Blue text means:
“This is an example of query-specific text or
values that might go into a SPARQL query.”
Nuts & Bolts
Write full URIs:
<http://this.is.a/full/URI/written#out>
Abbreviate URIs with prefixes:
PREFIX foo: <http://this.is.a/URI/prefix#>
… foo:bar …
 http://this.is.a/URI/prefix#bar
Shortcuts:
a  rdf:type
URIs
Plain literals:
“a plain literal”
Plain literal with language tag:
“bonjour”@fr
Typed literal:
“13”^^xsd:integer
Shortcuts:
true  “true”^^xsd:boolean
3  “3”^^xsd:integer
4.2  “4.2”^^xsd:decimal
Literals
Variables:
?var1, ?anotherVar, ?and_one_more
Variables
Comments:
# Comments start with a „#‟ and
# continue to the end of the line
Comments
Match an exact RDF triple:
ex:myWidget ex:partNumber “XY24Z1” .
Match one variable:
?person foaf:name “Lee Feigenbaum” .
Match multiple variables:
conf:SemTech2009 ?property ?value .
Triple Patterns
Common Prefixes
More common prefixes at http://prefix.cc
prefix... …stands for
rdf: http://xmlns.com/foaf/0.1/
rdfs: http://www.w3.org/2000/01/rdf-schema#
owl: http://www.w3.org/2002/07/owl#
xsd: http://www.w3.org/2001/XMLSchema#
dc: http://purl.org/dc/elements/1.1/
foaf: http://xmlns.com/foaf/0.1/
Anatomy of a Query
PREFIX foo: <…>
PREFIX bar: <…>
…
SELECT …
FROM <…>
FROM NAMED <…>
WHERE {
…
}
GROUP BY …
HAVING …
ORDER BY …
LIMIT …
OFFSET …
VALUES …
Declare prefix
shortcuts
(optional)
Query result
clause
Query pattern
Query modifiers
(optional)
Define the
dataset (optional)
4 Types of SPARQL Queries
Project out specific variables and expressions:
SELECT ?c ?cap (1000 * ?people AS ?pop)
Project out all variables:
SELECT *
Project out distinct combinations only:
SELECT DISTINCT ?country
Results in a table of values (in XML or JSON):
SELECT queries
?c ?cap ?pop
ex:France ex:Paris 63,500,000
ex:Canada ex:Ottawa 32,900,000
ex:Italy ex:Rome 58,900,000
Construct RDF triples/graphs:
CONSTRUCT {
?country a ex:HolidayDestination ;
ex:arrive_at ?capital ;
ex:population ?population .
}
Results in RDF triples (in any RDF serialization):
ex:France a ex:HolidayDestination ;
ex:arrive_at ex:Paris ;
ex:population 635000000 .
ex:Canada a ex:HolidayDestination ;
ex:arrive_at ex:Ottawa ;
ex:population 329000000 .
CONSTRUCT queries
Ask whether or not there are any matches:
ASK
Result is either “true” or “false” (in XML or JSON):
true, false
ASK queries
Describe the resources matched by the given variables:
DESCRIBE ?country
Result is RDF triples (in any RDF serialization) :
ex:France a geo:Country ;
ex:continent geo:Europe ;
ex:flag <http://…/flag-france.png> ;
…
DESCRIBE queries
Combining SPARQL Graph Patterns
Consider A and B as graph patterns.
A Basic Graph Pattern – one or more triple patterns
A . B
 Conjunction. Join together the results of solving A and B by matching the
values of any variables in common.
Optional Graph Patterns
A OPTIONAL { B }
 Left join. Join together the results of solving A and B by matching the
values of any variables in common, if possible. Keep all solutions from A whether or
not there’s a matching solution in B
Combining SPARQL Graph Patterns
Consider A and B as graph patterns.
Either-or Graph Patterns
{ A } UNION { B }
 Disjunction. Include both the results of solving A and the results of
solving B.
“Subtracted” Graph Patterns (SPARQL 1.1)
A MINUS { B }
 Negation. Solve A. Solve B. Include only those results from solving A that
are not compatible with any of the results from B.
SPARQL Subqueries (SPARQL 1.1)
Consider A and B as graph patterns.
A .
{
SELECT …
WHERE {
B
}
}
C .
 Join the results of the subquery with the results of solving A and C.
SPARQL Filters
• SPARQL FILTERs eliminate solutions that do not cause an
expression to evaluate to true.
• Place FILTERs in a query inline within a basic graph pattern
A . B . FILTER ( …expr… )
Category Functions / Operators Examples
Logical &
Comparisons
!, &&, ||, =, !=, <, <=, >,
>=, IN, NOT IN
?hasPermit || ?age < 25
Conditionals
(SPARQL 1.1)
EXISTS, NOT EXISTS, IF,
COALESCE
NOT EXISTS { ?p foaf:mbox ?email }
Math
+, -, *, /, abs, round,
ceil, floor, RAND
?decimal * 10 > ?minPercent
Strings
(SPARQL 1.1)
STRLEN, SUBSTR, UCASE,
LCASE, STRSTARTS, CONCAT,
STRENDS, CONTAINS,
STRBEFORE, STRAFTER
STRLEN(?description) < 255
Date/time
(SPARQL 1.1)
now, year, month, day,
hours, minutes, seconds,
timezone, tz
month(now()) < 4
SPARQL tests
isURI, isBlank,
isLiteral, isNumeric,
bound
isURI(?person) || !bound(?person)
Constructors
(SPARQL 1.1)
URI, BNODE, STRDT,
STRLANG, UUID, STRUUID
STRLANG(?text, “en”) = “hello”@en
Accessors str, lang, datatype lang(?title) = “en”
Hashing (1.1) MD5, SHA1, SHA256, SHA512 BIND(SHA256(?email) AS ?hash)
Miscellaneous
sameTerm, langMatches,
regex, REPLACE
regex(?ssn, “d{3}-d{2}-d{4}”)
Aggregates (SPARQL 1.1)
1. Partition results into
groups based on the
expression(s) in the
GROUP BY clause
2. Evaluate projections
and aggregate functions
in SELECT clause to get
one result per group
3. Filter aggregated
results via the HAVING
clause
?key ?val ?other1
1 4 …
1 4 …
2 5 …
2 4 …
2 10 …
2 2 …
2 1 …
3 3 …
?key ?sum_of_val
1 8
2 22
3 3
?key ?sum_of_val
1 8
3 3
SPARQL 1.1 includes: COUNT, SUM, AVG, MIN, MAX, SAMPLE, GROUP_CONCAT
Property Paths (SPARQL 1.1)
• Property paths allow triple patterns to match arbitrary-
length paths through a graph
• Predicates are combined with regular-expression-like
operators:
Construct Meaning
path1/path2 Forwards path (path1 followed by path2)
^path1 Backwards path (object to subject)
path1|path2 Either path1 or path2
path1* path1, repeated zero or more times
path1+ path1, repeated one or more times
path1? path1, optionally
!uri Any predicate except uri
!^uri Any backwards (object to subject) predicate except uri
RDF Datasets
A SPARQL queries a default graph (normally) and zero or
more named graphs (when inside a GRAPH clause).
ex:g1
ex:g2
ex:g3
Default graph
(the merge of zero or more graphs)
Named graphs
ex:g1
ex:g4
PREFIX ex: <…>
SELECT …
FROM ex:g1
FROM ex:g4
FROM NAMED ex:g1
FROM NAMED ex:g2
FROM NAMED ex:g3
WHERE {
… A …
GRAPH ex:g3 {
… B …
}
GRAPH ?graph {
… C …
}
}
OR
OR
SPARQL Over HTTP (the SPARQL Protocol)
http://host.domain.com/sparql/endpoint?<parameters>
where <parameters> can include:
query=<encoded query string>
e.g. SELECT+*%0DWHERE+{…
default-graph-uri=<encoded graph URI>
e.g. http%3A%2F%2Fexmaple.com%2Ffoo…
n.b. zero of more occurrences of default-graph-uri
named-graph-uri=<encoded graph URI>
e.g. http%3A%2F%2Fexmaple.com%2Fbar…
n.b. zero of more occurrences of named-graph-uri
HTTP GET or POST. Graphs given in the protocol override graphs given in the
query.
Federated Query (SPARQL 1.1)
PREFIX ex: <…>
SELECT …
FROM ex:g1
WHERE {
… A …
SERVICE ex:s1 {
… B …
}
SERVICE ex:s2 {
… C …
}
}
ex:g1
Web
SPARQL Endpoint
ex:s2
SPARQL Endpoint
ex:s1
Local Graph Store
SPARQL 1.1 Update
SPARQL Update Language Statements
INSERT DATA { triples }
DELETE DATA {triples}
[ DELETE { template } ] [ INSERT { template } ] WHERE { pattern }
LOAD <uri> [ INTO GRAPH <uri> ]
CLEAR GRAPH <uri>
CREATE GRAPH <uri>
DROP GRAPH <uri>
[ … ] denotes optional parts of SPARQL 1.1 Update syntax
Some Public SPARQL Endpoints
Name URL What’s there?
SPARQLer http://sparql.org/sparql.html
General-purpose query
endpoint for Web-accessible
data
DBPedia http://dbpedia.org/sparql
Extensive RDF data from
Wikipedia
DBLP http://www4.wiwiss.fu-berlin.de/dblp/snorql/
Bibliographic data from
computer science journals
and conferences
LinkedMDB http://data.linkedmdb.org/sparql
Films, actors, directors,
writers, producers, etc.
World
Factbook
http://www4.wiwiss.fu-
berlin.de/factbook/snorql/
Country statistics from the
CIA World Factbook
bio2rdf http://bio2rdf.org/sparql
Bioinformatics data from
around 40 public databases
SPARQL Resources
• SPARQL Specifications Overview
– http://www.w3.org/TR/sparql11-overview/
• SPARQL implementations
– http://esw.w3.org/topic/SparqlImplementations
• SPARQL endpoints
– http://esw.w3.org/topic/SparqlEndpoints
• SPARQL Frequently Asked Questions
– http://www.thefigtrees.net/lee/sw/sparql-faq
• Common SPARQL extensions
– http://esw.w3.org/topic/SPARQL/Extensions

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SPARQL Cheat Sheet

  • 1. SPARQL By Example: The Cheat Sheet Accompanies slides at: http://www.cambridgesemantics.com/semantic-university/sparql-by-example Comments & questions to: Lee Feigenbaum <lee@cambridgesemantics.com> VP Marketing & Technology, Cambridge Semantics Co-chair, W3C SPARQL Working Group
  • 2. Conventions Red text means: “This is a core part of the SPARQL syntax or language.” Blue text means: “This is an example of query-specific text or values that might go into a SPARQL query.”
  • 3. Nuts & Bolts Write full URIs: <http://this.is.a/full/URI/written#out> Abbreviate URIs with prefixes: PREFIX foo: <http://this.is.a/URI/prefix#> … foo:bar …  http://this.is.a/URI/prefix#bar Shortcuts: a  rdf:type URIs Plain literals: “a plain literal” Plain literal with language tag: “bonjour”@fr Typed literal: “13”^^xsd:integer Shortcuts: true  “true”^^xsd:boolean 3  “3”^^xsd:integer 4.2  “4.2”^^xsd:decimal Literals Variables: ?var1, ?anotherVar, ?and_one_more Variables Comments: # Comments start with a „#‟ and # continue to the end of the line Comments Match an exact RDF triple: ex:myWidget ex:partNumber “XY24Z1” . Match one variable: ?person foaf:name “Lee Feigenbaum” . Match multiple variables: conf:SemTech2009 ?property ?value . Triple Patterns
  • 4. Common Prefixes More common prefixes at http://prefix.cc prefix... …stands for rdf: http://xmlns.com/foaf/0.1/ rdfs: http://www.w3.org/2000/01/rdf-schema# owl: http://www.w3.org/2002/07/owl# xsd: http://www.w3.org/2001/XMLSchema# dc: http://purl.org/dc/elements/1.1/ foaf: http://xmlns.com/foaf/0.1/
  • 5. Anatomy of a Query PREFIX foo: <…> PREFIX bar: <…> … SELECT … FROM <…> FROM NAMED <…> WHERE { … } GROUP BY … HAVING … ORDER BY … LIMIT … OFFSET … VALUES … Declare prefix shortcuts (optional) Query result clause Query pattern Query modifiers (optional) Define the dataset (optional)
  • 6. 4 Types of SPARQL Queries Project out specific variables and expressions: SELECT ?c ?cap (1000 * ?people AS ?pop) Project out all variables: SELECT * Project out distinct combinations only: SELECT DISTINCT ?country Results in a table of values (in XML or JSON): SELECT queries ?c ?cap ?pop ex:France ex:Paris 63,500,000 ex:Canada ex:Ottawa 32,900,000 ex:Italy ex:Rome 58,900,000 Construct RDF triples/graphs: CONSTRUCT { ?country a ex:HolidayDestination ; ex:arrive_at ?capital ; ex:population ?population . } Results in RDF triples (in any RDF serialization): ex:France a ex:HolidayDestination ; ex:arrive_at ex:Paris ; ex:population 635000000 . ex:Canada a ex:HolidayDestination ; ex:arrive_at ex:Ottawa ; ex:population 329000000 . CONSTRUCT queries Ask whether or not there are any matches: ASK Result is either “true” or “false” (in XML or JSON): true, false ASK queries Describe the resources matched by the given variables: DESCRIBE ?country Result is RDF triples (in any RDF serialization) : ex:France a geo:Country ; ex:continent geo:Europe ; ex:flag <http://…/flag-france.png> ; … DESCRIBE queries
  • 7. Combining SPARQL Graph Patterns Consider A and B as graph patterns. A Basic Graph Pattern – one or more triple patterns A . B  Conjunction. Join together the results of solving A and B by matching the values of any variables in common. Optional Graph Patterns A OPTIONAL { B }  Left join. Join together the results of solving A and B by matching the values of any variables in common, if possible. Keep all solutions from A whether or not there’s a matching solution in B
  • 8. Combining SPARQL Graph Patterns Consider A and B as graph patterns. Either-or Graph Patterns { A } UNION { B }  Disjunction. Include both the results of solving A and the results of solving B. “Subtracted” Graph Patterns (SPARQL 1.1) A MINUS { B }  Negation. Solve A. Solve B. Include only those results from solving A that are not compatible with any of the results from B.
  • 9. SPARQL Subqueries (SPARQL 1.1) Consider A and B as graph patterns. A . { SELECT … WHERE { B } } C .  Join the results of the subquery with the results of solving A and C.
  • 10. SPARQL Filters • SPARQL FILTERs eliminate solutions that do not cause an expression to evaluate to true. • Place FILTERs in a query inline within a basic graph pattern A . B . FILTER ( …expr… )
  • 11. Category Functions / Operators Examples Logical & Comparisons !, &&, ||, =, !=, <, <=, >, >=, IN, NOT IN ?hasPermit || ?age < 25 Conditionals (SPARQL 1.1) EXISTS, NOT EXISTS, IF, COALESCE NOT EXISTS { ?p foaf:mbox ?email } Math +, -, *, /, abs, round, ceil, floor, RAND ?decimal * 10 > ?minPercent Strings (SPARQL 1.1) STRLEN, SUBSTR, UCASE, LCASE, STRSTARTS, CONCAT, STRENDS, CONTAINS, STRBEFORE, STRAFTER STRLEN(?description) < 255 Date/time (SPARQL 1.1) now, year, month, day, hours, minutes, seconds, timezone, tz month(now()) < 4 SPARQL tests isURI, isBlank, isLiteral, isNumeric, bound isURI(?person) || !bound(?person) Constructors (SPARQL 1.1) URI, BNODE, STRDT, STRLANG, UUID, STRUUID STRLANG(?text, “en”) = “hello”@en Accessors str, lang, datatype lang(?title) = “en” Hashing (1.1) MD5, SHA1, SHA256, SHA512 BIND(SHA256(?email) AS ?hash) Miscellaneous sameTerm, langMatches, regex, REPLACE regex(?ssn, “d{3}-d{2}-d{4}”)
  • 12. Aggregates (SPARQL 1.1) 1. Partition results into groups based on the expression(s) in the GROUP BY clause 2. Evaluate projections and aggregate functions in SELECT clause to get one result per group 3. Filter aggregated results via the HAVING clause ?key ?val ?other1 1 4 … 1 4 … 2 5 … 2 4 … 2 10 … 2 2 … 2 1 … 3 3 … ?key ?sum_of_val 1 8 2 22 3 3 ?key ?sum_of_val 1 8 3 3 SPARQL 1.1 includes: COUNT, SUM, AVG, MIN, MAX, SAMPLE, GROUP_CONCAT
  • 13. Property Paths (SPARQL 1.1) • Property paths allow triple patterns to match arbitrary- length paths through a graph • Predicates are combined with regular-expression-like operators: Construct Meaning path1/path2 Forwards path (path1 followed by path2) ^path1 Backwards path (object to subject) path1|path2 Either path1 or path2 path1* path1, repeated zero or more times path1+ path1, repeated one or more times path1? path1, optionally !uri Any predicate except uri !^uri Any backwards (object to subject) predicate except uri
  • 14. RDF Datasets A SPARQL queries a default graph (normally) and zero or more named graphs (when inside a GRAPH clause). ex:g1 ex:g2 ex:g3 Default graph (the merge of zero or more graphs) Named graphs ex:g1 ex:g4 PREFIX ex: <…> SELECT … FROM ex:g1 FROM ex:g4 FROM NAMED ex:g1 FROM NAMED ex:g2 FROM NAMED ex:g3 WHERE { … A … GRAPH ex:g3 { … B … } GRAPH ?graph { … C … } } OR OR
  • 15. SPARQL Over HTTP (the SPARQL Protocol) http://host.domain.com/sparql/endpoint?<parameters> where <parameters> can include: query=<encoded query string> e.g. SELECT+*%0DWHERE+{… default-graph-uri=<encoded graph URI> e.g. http%3A%2F%2Fexmaple.com%2Ffoo… n.b. zero of more occurrences of default-graph-uri named-graph-uri=<encoded graph URI> e.g. http%3A%2F%2Fexmaple.com%2Fbar… n.b. zero of more occurrences of named-graph-uri HTTP GET or POST. Graphs given in the protocol override graphs given in the query.
  • 16. Federated Query (SPARQL 1.1) PREFIX ex: <…> SELECT … FROM ex:g1 WHERE { … A … SERVICE ex:s1 { … B … } SERVICE ex:s2 { … C … } } ex:g1 Web SPARQL Endpoint ex:s2 SPARQL Endpoint ex:s1 Local Graph Store
  • 17. SPARQL 1.1 Update SPARQL Update Language Statements INSERT DATA { triples } DELETE DATA {triples} [ DELETE { template } ] [ INSERT { template } ] WHERE { pattern } LOAD <uri> [ INTO GRAPH <uri> ] CLEAR GRAPH <uri> CREATE GRAPH <uri> DROP GRAPH <uri> [ … ] denotes optional parts of SPARQL 1.1 Update syntax
  • 18. Some Public SPARQL Endpoints Name URL What’s there? SPARQLer http://sparql.org/sparql.html General-purpose query endpoint for Web-accessible data DBPedia http://dbpedia.org/sparql Extensive RDF data from Wikipedia DBLP http://www4.wiwiss.fu-berlin.de/dblp/snorql/ Bibliographic data from computer science journals and conferences LinkedMDB http://data.linkedmdb.org/sparql Films, actors, directors, writers, producers, etc. World Factbook http://www4.wiwiss.fu- berlin.de/factbook/snorql/ Country statistics from the CIA World Factbook bio2rdf http://bio2rdf.org/sparql Bioinformatics data from around 40 public databases
  • 19. SPARQL Resources • SPARQL Specifications Overview – http://www.w3.org/TR/sparql11-overview/ • SPARQL implementations – http://esw.w3.org/topic/SparqlImplementations • SPARQL endpoints – http://esw.w3.org/topic/SparqlEndpoints • SPARQL Frequently Asked Questions – http://www.thefigtrees.net/lee/sw/sparql-faq • Common SPARQL extensions – http://esw.w3.org/topic/SPARQL/Extensions