程序代写代做代考 database prolog SQL RDF and SPARQL

RDF and SPARQL

Miao Qiao

SEAT
Massey University

Data Model: RDF

Why we need RDF? Semantic Web.
“The Semantic Web is an extension of the current web in which
information is given well-defined meaning, better enabling com-
puters and people to work in cooperation.”

(Tim Berners-Lee et al. 2001.)

Specific goals:
• A descriptive language with standard semantics.

Make semantics machine-processable and understandable.
• Incorporate logical infrastructure to reason about resources.
• W3C proposals: Resource Description Framework (RDF) and its
query language SPARQL.

RDF and SPARQL

• Data Model: RDF
• Query Language: SPARQL

Data Model: RDF

Resource Description Framework (RDF)
RDF is a graph-based model for representing the web as a graph of
• Resources and their properties, using
• Internationalized Resource Identifiers (IRIs) a and literal values.

IRI: http://example.org/123
Literal: 438sd:int

aA generalized Unified Resource Identifier that permits more characters.

The graph is a collection of subject-predicate-object triples (RDF triples).

http://example.org/123

Data Model: RDF

• RDF Statement : an RDF triple.
• An RDF triple:

Subject :IRI or blank node.
Predicate : IRI.
Object : IRI, blank node or literal.

Data Model: RDF

• RDF Statement : an RDF triple.
• An RDF triple:

Subject :IRI or blank node.
Predicate : IRI.
Object : IRI, blank node or literal.

Data Model: RDF
One can write RDF in different syntaxes:
• RDF/XML: respent RDF data in XML.
• Turtle: a compact presentation of RDF data in RDF triples.

Data Model: RDF
RDF document can be presented in different syntaxes.
• RDF/XML: respent RDF data in XML.
• Turtle: a compact presentation of RDF data in RDF triples.

@prefix rdf: .
@prefix dc: .
@prefix ex: .

dc:title “RDF/XML Syntax” ;
ex:editors [

ex:fullname “Dave Beckett”;
ex:homePage

] .

Data Model: RDF

RDF document can be presented in different syntaxes.
• RDF/XML: respent RDF data in XML.
• Turtle: a compact presentation of RDF data in RDF triples.

Next, we shall mainly use the Turtle syntax to describe an RDF document.

Data Model: RDF

RDF Schema
RDF Schema standardizes RDF vocabulary for describing classes and
properties
• using object-oriented types + domain/range
• subclasses (sc), subproperties (sp), domain/range, · · ·

Data Model: RDF

RDF Schema
RDF Schema standardizes RDF vocabulary for describing classes and
properties
• using object-oriented types + domain/range
• subclasses (sc), subproperties (sp), domain/range, · · ·

Range and domain:
• A rdfs:domain B

A is an instance of the class rdf:Property.
B is a instance of the class rdfs:Class.
The subjects of triples whose predicate is A are the instances of B.

• A rdfs:range B
A is an instance of the class rdf:Property.
B is a instance of the class rdfs:Class.
The objects of triples whose predicate is A are the instances of B.

Data Model: RDF

RDF Schema
RDF Schema standardizes RDF vocabulary for describing classes and
properties
• using object-oriented types + domain/range
• subclasses (sc), subproperties (sp), domain/range, · · ·

Data Model: RDF

RDF Schema
RDF Schema standardizes RDF vocabulary for describing classes and
properties
• using object-oriented types + domain/range
• subclasses (sc), subproperties (sp), domain/range, · · ·

The semantics can be used as an inference system.
• Sub-property:

(A, sp,B) + (B, sp,C) => (A, sp,C)
(A, sp,B) + (X ,A,Y ) => (X ,B,Y )

• Subclass:
(A, sc,B) + (B, sc,C) => (A, sc,C)
(A, sc,B) + (X , type,A) => (X , type,B)

• Typing:
(A, dom,B) + (X ,A,Y ) => (X , type,B)
(A, range,B) + (X ,A,Y ) => (Y , type,B)

Data Model: RDF

prefix Namespace IRI RDF vocabulary
rdfs http://www.w3.org/2000/01/rdf-schema# The RDF Schema vocabulary
rdf http://www.w3.org/1999/02/22-rdf-syntax-ns# The RDF built-in vocabulary

rdfs

is used to define
=⇒ rdf

rdfs
rdf

}
are used to define

=⇒ other vocabularies such as dc, owl, · · ·

http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/1999/02/22-rdf-syntax-ns#

Data Model: RDF

prefix Namespace IRI RDF vocabulary
rdfs http://www.w3.org/2000/01/rdf-schema# The RDF Schema vocabulary
rdf http://www.w3.org/1999/02/22-rdf-syntax-ns# The RDF built-in vocabulary

rdfs
is used to define

=⇒ rdf

rdfs
rdf

}
are used to define

=⇒ other vocabularies such as dc, owl, · · ·

http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/1999/02/22-rdf-syntax-ns#

Data Model: RDF

prefix Namespace IRI RDF vocabulary
rdfs http://www.w3.org/2000/01/rdf-schema# The RDF Schema vocabulary
rdf http://www.w3.org/1999/02/22-rdf-syntax-ns# The RDF built-in vocabulary

rdfs
is used to define

=⇒ rdf
rdfs
rdf

}
are used to define

=⇒ other vocabularies such as dc, owl, · · ·

http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/1999/02/22-rdf-syntax-ns#

Data Model: RDF

prefix Namespace IRI RDF vocabulary
rdfs http://www.w3.org/2000/01/rdf-schema# The RDF Schema vocabulary
rdf http://www.w3.org/1999/02/22-rdf-syntax-ns# The RDF built-in vocabulary

rdfs
is used to define

=⇒ rdf
rdfs
rdf

}
are used to define

=⇒ other vocabularies such as dc, owl, · · ·

http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/1999/02/22-rdf-syntax-ns#

Data Model: RDF

• Namespace URI: the common substring of the URIs in an RDF
vocabulary.

prefix Namespace IRI RDF vocabulary
rdf http://www.w3.org/1999/02/22-rdf-syntax-ns# The RDF built-in vocabulary
rdfs http://www.w3.org/2000/01/rdf-schema# The RDF Schema vocabulary
xsd http://www.w3.org/2001/XMLSchema# The RDF-compatible XSD types
dc http://purl.org/dc/elements/1.1# “Dublin Core Metadata Element Set
eg http://example.org/ the IRI defined locally.

http://www.w3.org/1999/02/22-rdf-syntax-ns#
http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/2001/XMLSchema#
http://purl.org/dc/elements/1.1#
http://example.org/

Data Model: RDF

• Namespace URI: the common substring of the URIs in an RDF
vocabulary.

http://www.w3.org/1999/02/22-rdf-syntax-ns#
http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/2001/XMLSchema#
http://purl.org/dc/elements/1.1#
http://example.org/

Data Model: RDF
Ontology = ontos(being) + logos(word).
• An ontology is a systematic explanation of existence (400BC).
• Grube (1998)

Formal: machine readable
Explicit: specifications of concepts, properties, functions, axioms are
defined.
Shared: consensual knowledge
Conceptual: abstract model of some phenomena in the world.

Example: Define the concept of “MusicalArtist”. http://dbpedia.org/fct/

http://dbpedia.org/fct/

Data Model: RDF
Ontology = ontos(being) + logos(word).
• An ontology is a systematic explanation of existence (400BC).
• Grube (1998)

Formal: machine readable
Explicit: specifications of concepts, properties, functions, axioms are
defined.
Shared: consensual knowledge
Conceptual: abstract model of some phenomena in the world.

Example: Define the concept of “MusicalArtist”. http://dbpedia.org/fct/

http://dbpedia.org/fct/

RDF and SPARQL

• Data Model: RDF
• Query Language: SPARQL

Querying RDF: SPARQL

An SQL query retrieves data from a database under the relational model.
SELECT name data needed
FROM student data source
WHERE age > 20 data constraint

SPARQL
• a recursive acronym that stands for
SPARQL Protocol and RDF Query Language
• a graph-matching query language for RDF graphs.
• recommended by W3C
• Like SQL, SPARQL consists of three components:

select: the entities to be returned
from: the data source (RDF graph)
where: the pattern to be matched against the RDF graph
optional prologue: namespace

Querying RDF: SPARQL

Example: find 10 films with the keyword of “Paddington” in thier labels.
prefix rdfs:
prefix dbpedia-owl:
prefix movie:

select distinct ?film where {
{ ?film a movie:Film } union
{ ?film a dbpedia-owl:Film }
?film rdfs:label ?label .
filter regex( str(?label), “Paddington”, “i”)

}
limit 10

Try this on the SPARQL endPoint of dbpedia
https://dbpedia.org/sparql.

https://dbpedia.org/sparql

Query Language: SPARQL

The WHERE clause specifies the graph pattern which :
• is an RDF graph
• involves variables
• has the basic pattern of triple pattern
• can be complex

Semantic: for a pattern P and an RDF graph G , we denote by [P]G the
set of matchings of P, that are, subgraphs of G that match P.

Query Language: SPARQL

Triple pattern

Example: A triple pattern can have one/more variables.
• ?film rdfs:label ?label
• ?film a movie:film

Note: a is the abbreviation of rdfs:type.

select ?film where {
?film a dbpedia-owl:Film

} limit 10

https://dbpedia.org/sparql.

https://dbpedia.org/sparql

Query Language: SPARQL

A larger pattern is assembled with triple patterns.
The queries below are equivalent.

select ?film
where {

{?film a movie:Film }
{?film rdfs:label ?label }

}

select ?film
where {

?film a movie:Film .
?film rdfs:label ?label .

}

Semantics: [P1 AND P2]G = [P1]G [P2]G

Query Language: SPARQL

Alternative Graphs: Union

select ?film
where {

{?film a movie:Film } UNION
{?film rdfs:label ?label}

}

Return the films that EITHER satisfy the pattern of
• ?film a movie:Film or
• ?film rdfs:label ?label

Semantic: [P1UNIONP2] = [P1] ∪ [P2].

Query Language: SPARQL

Optional pattern:

select ?film ?label
where {

{?film a movie:Film } OPTIONAL
{?film rdfs:label ?label}

}

Return the films that satisfy the pattern of
• ?film a movie:Film

and return its labels if there are any.

Semantics: [P1 OPT P2]G = [P1]G [P2]G

Query Language: SPARQL
Filter clause: a boolean expression consists of
• RDF-model related operators

is Literal(?aNode)
isURI(?aNode)
· · ·

• Numeric values comparison
• Literal: regex (from XQuery)

select ?film where {
?film rdfs:label ?label .
filter regex( str(?label), “Paddington”, “i”)

}

• xsd:boolean REGEX (string literal text, simple literal pattern, flags)
• flags

i: case-insensitive
See https://www.w3.org/TR/xpath-functions/#regex-syntax for more information.

https://www.w3.org/TR/xpath-functions/#regex-syntax

Query Language: SPARQL

Filter clause: a boolean expression consists of
• RDF-model related operators

is Literal(?aNode)
isURI(?aNode)
· · ·

• Numeric values comparison
• Literal: regex (from XQuery)

select ?film where {
?film rdfs:label ?label .
filter regex( str(?label), “Paddington”, “i”)

}

Semantics: [P FILTER R]G = {µ ∈ [P]G | µ |= R}

Query Language: SPARQL

The pattern of a SPARQL query can be complicated.
Let P1 to P9 be triple patterns and R be a regular expression in XQuery.

• Grouping
• Optional parts
• Nesting
• Union of patterns
• Filtering

{

{ P1
P2 }

OPTIONAL { P5 } }

{ P3
P4
OPTIONAL { P7

OPTIONAL { P8 } } }
}
UNION
{ P9

FILTER ( R ) }

Query Language: SPARQL

The pattern of a SPARQL query can be complicated.
Let P1 to P9 be triple patterns and R be a regular expression in XQuery.

• Grouping

• Optional parts
• Nesting
• Union of patterns
• Filtering

{ { P1
P2 }

OPTIONAL { P5 } }

{ P3
P4 }

OPTIONAL { P7
OPTIONAL { P8 } } }

}

UNION
{ P9

FILTER ( R ) }

Query Language: SPARQL

The pattern of a SPARQL query can be complicated.
Let P1 to P9 be triple patterns and R be a regular expression in XQuery.

• Grouping
• Optional parts

• Nesting
• Union of patterns
• Filtering

{ { P1
P2
OPTIONAL { P5 } }

{ P3
P4
OPTIONAL { P7 } }

OPTIONAL { P8 } } }

}

UNION
{ P9

FILTER ( R ) }

Query Language: SPARQL

The pattern of a SPARQL query can be complicated.
Let P1 to P9 be triple patterns and R be a regular expression in XQuery.

• Grouping
• Optional parts
• Nesting

• Union of patterns
• Filtering

{ { P1
P2
OPTIONAL { P5 } }

{ P3
P4
OPTIONAL { P7

OPTIONAL { P8 } } }
}

UNION
{ P9

FILTER ( R ) }

Query Language: SPARQL

The pattern of a SPARQL query can be complicated.
Let P1 to P9 be triple patterns and R be a regular expression in XQuery.

• Grouping
• Optional parts
• Nesting
• Union of patterns

• Filtering

{ { P1
P2
OPTIONAL { P5 } }

{ P3
P4
OPTIONAL { P7

OPTIONAL { P8 } } }
}
UNION
{ P9 }

FILTER ( R ) }

Query Language: SPARQL

The pattern of a SPARQL query can be complicated.
Let P1 to P9 be triple patterns and R be a regular expression in XQuery.

• Grouping
• Optional parts
• Nesting
• Union of patterns
• Filtering

{ { P1
P2
OPTIONAL { P5 } }

{ P3
P4
OPTIONAL { P7

OPTIONAL { P8 } } }
}
UNION
{ P9

FILTER ( R ) }

Query Language: SPARQL

Let P1 and P9 be triple patterns and R be a regular expression in XQuery.
• Graph patterns: full parenthesized algebra

{ P1 P2 } (P1 AND P2 )

{ P1 OPTIONAL { P2 }} (P1 OPT P2 )

{ P1 } UNION { P2 } (P1 UNION P2 )

{ P1 FILTER ( R ) } (P1 FILTER R )

original SPARQL syntax algebraic syntax

Query Language: SPARQL

Semantics of SPARQL
Given an RDF graph G .

Let t be a triple pattern and P1 and P2 be two patterns

Definition
[t]G =

the subgraphs of G that matches t.

[P1 AND P2]G =

[P1]G [P2]G

[P1 UNION P2]G =

[P1]G ∪ [P2]G

[P1 OPT P2]G =

[P1]G [P2]G

Query Language: SPARQL

Semantics of SPARQL
Given an RDF graph G .

Let t be a triple pattern and P1 and P2 be two patterns

Definition
[t]G = the subgraphs of G that matches t.
[P1 AND P2]G = [P1]G [P2]G
[P1 UNION P2]G = [P1]G ∪ [P2]G
[P1 OPT P2]G = [P1]G [P2]G

Query Language: SPARQL
Solution Modifiers.
• ORDER BY
• LIMIT
• DISTINCT
• PROJECTION
• OFFSET: control where the solutions start from in the overall
sequence of solutions
• REDUCED: permit any non-unique solutions to be eliminated

prefix rdfs:
prefix dbpedia-owl:
prefix movie:

select distinct ?film where {
{ ?film a movie:Film } union
{ ?film a dbpedia-owl:Film }
?film rdfs:label ?label .
filter regex( str(?label), “Paddington”, “i”)

}
order by ?film
limit 10

Query Language: SPARQL

SPARQL can be used to:

• select
• construct an RDF graph construct{a triple} where{}

• test whether a query pattern has a solution ask{}

• describe a resource with an RDF graph describe aResource

Query Language: SPARQL

Example: find 10 films with the keyword of “Paddington” in their labels.
prefix rdfs:
prefix dbpedia-owl:
prefix movie:

select distinct ?film where {
{ ?film a movie:Film } union
{ ?film a dbpedia-owl:Film }
?film rdfs:label ?label .
filter regex( str(?label), “Paddington”, “i”)

}
limit 10

Query Language: SPARQL

Example of AND , UNION : find 10 films either with the keyword of
“Paddington” in their labels or acted by Tom Cruise.
prefix rdfs:
prefix dbpedia-owl:
prefix movie:

select distinct ?film where {
{ ?film a movie:Film } union
{ ?film a dbpedia-owl:Film }
{?film rdfs:label ?label .
filter regex( str(?label), “Paddington”, “i”)} union
{?film dbo:starring ?actor
filter regex( str(?actor), “Tom_Cruise”, “i”)}

}
limit 10

Query Language: SPARQL

Example of OPT : find 10 films with the keyword of “Paddington” in
thier labels and show if they are acted by Hugh Grant.
prefix rdfs:
prefix dbpedia-owl:
prefix movie:

select distinct ?film ?actor where {
{ ?film a movie:Film } union
{ ?film a dbpedia-owl:Film }
{?film rdfs:label ?label .
filter regex( str(?label), “Paddington”, “i”)} optional
{?film dbo:starring ?actor
filter regex( str(?actor), “Hugh_Grant”, “i”)}

}
limit 10

RDF and SPARQL

• Data Model: RDF
• Query Language: SPARQL

Resources:
• Prefixes: http://dbpedia.org/sparql?help=nsdecl
• DBpedia: http://dbpedia.org/sparql
• W3C: https://www.w3.org/TR/rdf-sparql-query/

http://dbpedia.org/sparql?help=nsdecl
http://dbpedia.org/sparql
https://www.w3.org/TR/rdf-sparql-query/

References

• http://www.iro.umontreal.ca/~lapalme/ift6281/sparql-1_
1-cheat-sheet.pdf

• http://webkr.cs.vu.nl/slides/rdfs.pdf
• http://marenas.sitios.ing.uc.cl/talks/pods11.pdf

http://www.iro.umontreal.ca/~lapalme/ift6281/sparql-1_1-cheat-sheet.pdf
http://www.iro.umontreal.ca/~lapalme/ift6281/sparql-1_1-cheat-sheet.pdf
http://webkr.cs.vu.nl/slides/rdfs.pdf
http://marenas.sitios.ing.uc.cl/talks/pods11.pdf

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