(04) Semantic Web Technologies - SPARQL

Semantic Web Technologies

LectureDr. Harald Sack

Hasso-Plattner-Institut für IT Systems EngineeringUniversity of Potsdam

Winter Semester 2012/13

Lecture Blog: http://semweb2013.blogspot.com/This file is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0)

Dienstag, 6. November 12

http://semweb2013.blogspot.com


http://creativecommons.org/licenses/by-nc/3.0/

http://creativecommons.org/licenses/by-nc/3.0/

Semantic Web Technologies , Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam

2 1. Introduction 2. Semantic Web - Basic Architecture

Languages of the Semantic Web - Part 1

3. Knowledge Representation and LogicsLanguages of the Semantic Web - Part 2

4. Applications in the ,Web of Data‘

Semantic Web Technologies Content



3

SO ... WE NEED A WAY TO DEFINE RDF SCHEMATA FORCLASSES AND RELATIONS ?!

RDFS

Last lecture...



4


2. Semantic Web Basic ArchitectureThe Languages of the Semantic Web - Part 1

2.1.Uniform Resource Identifier - URI2.2.Recapitulation: XML and XMLSchema2.3.Resource Description Framework - RDF2.4.RDF Schema2.5.How Do We Get Semantics into WWW?2.6.Query Language SPARQL2.7.Why RDF(S) is not enough



5

via http://www.snee.com/bobdc.blog/2008/10/learning-more-about-sparql.html

SPARQL



6




7 A Query Language for RDF• How do we get to the knowledge from RDF/RDFS Knowledge Bases? • Manual Parsing/Reading and Combination of RDF-Triple is complex

• Can you imagine a relational database without SQL?

2. Semantic Web Basic Architecture2.6 Query Language SPARQL

• SPARQL Protocol and RDF Query Language ist• a Query Language for RDF Graph TRaversal

(SPARQL Query Language Specification)• a Protocol Layer, to use SPARQL via http

(SPARQL Protocol for RDF Specification)• an XML Output Format Specification for SPARQL Queries

(SPARQL Query XML Results Format)• W3C Standard (SPARQL 1.0 since Januar 2008) • inspired by SQL



8 •SPARQL 1.0 allows

•Extraction of data from structured or semi structures Data as

•URIs, Blank Nodes, typed and untyped Literals.

•RDF Subgraphs

•Exploration of Data via Query for unknown relations

•Execution of complex Join Operations on heterogeneous databases in a single query

•Transformation of RDF Data from one vocabulary into another

•Construction of new RDF Graphs based on RDF Query Graphs



9 •SPARQL 1.1 (in progress) allows

•additional query features

•aggregate functions, subqueries, negations, project expressions, property paths, basic federated queries

•enables logical Entailment for

•RDF, RDFS, OWL Direct and RDF-Based Semantics entailment, and RIF Core entailment.

•enables Update of RDF Graphs as a full data manipulation language

•enables the Discovery of information about the SPARQL service

•enables Federated Queries distributed over different SPARQL



10

SPARQL 1.0 firstDienstag, 6. November 12


11 • SPARQL Variables are bound to RDF terms• e.g. ?journal, ?disease, ?price

• In the same way as in SQL a Query for variables is performed via SELECT statement• e.g. SELECT ?title ?author ?published

• A SELECT statement returns Query Results as a table

?title ?author ?published

1984 George Orwell 1948

Brave New World Aldous Huxley 1932

Fahrenheid 451 Ray Bradbury 1953



12

?title ?author ?published

1984 George Orwell 1948

Brave New World Aldous Huxley 1932

Fahrenheid 451 Ray Bradbury 1953

SELECT ?title ?author ?published

SPARQL Query

SPARQL Endpoint



13 Graph Pattern• SPARQL is based on RDF Turtle serialization and

basic graph pattern matching.• A Graph Pattern (Triple Pattern) is a RDF Triple that contains

variables at any arbitrary place (Subject, Predicate, Object).• (Graph) Triple Pattern = Turtle + Variables• Examples:• Look for countries and their capitals:

•?country geo:capital ?capital .• Given a FOAF URI, find the name of a person:

•<http://hpi-web.de/id#haraldsack> foaf:name ?surname .• Which persons have the family name „Schmidt“?

•?person pers:familyName “Schmidt“ .



14

?country geo:capital ?capital .Triple Pattern

RDF Graphdbpedia:Venezuela rdf:type dbpedia-owl:Country .dbpedia:Venezuela geo:capital “Caracas“ .dbpedia:Venezuela dbprop:language “Spanish“ .dbpedia:Germany rdf:type dbpedia-owl:Country .dbpedia:Germany geo:capital “Berlin“ .dbpedia:Germany dbprop:language “German“ ....



15 Simple Query Pattern• Graph Pattern can be combined to form complex (conjunctive)

queries for RDF graph traversal.• Find countries, their capitals and their population:

•?country geo:capital ?capital .?country geo:population ?population .

• Given a FOAF URI, find the name of a person and her friends:•<http://hpi-web.de/id#haraldsack> foaf:name ?surname ; foaf:knows ?friend .?friend foaf:name ?friend_surname .

• Find all superiors of the 3. management level:•?emp hr:managedBy ?first_line .?first_line hr:managedBy ?second_line .?second_line hr:managedBy ?third_line .



16 SPARQL Query Format• inspired by SQL

• Triple in „WHERE“ part defines graph query with variables ?p and ?o• Query returns table with matching ?p, ?o pairs

SELECT ?p, ?oWHERE { subject ?p ?o. }

?o

?o

?o

?p

?o

?p

?p

?p

subject



17SPARQL Query Format• Search all lectures and their managers:

• WHERE - specifies graph pattern to be matched• PREFIX - specifies one or more namespaces for using compact URIs (CURIEs)• FROM - specifies one or more RDF source graphs• BASE - defines a base URI

PREFIX hpi: <http://hpi-web.de/swt1213#>PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>SELECT ?lecture ?managerFROM <...>WHERE { ?x rdf:type hpi:Lecture . ?x rdfs:label ?lecture . ?y rdf:type hpi:Staff . ?y rdfs:label ?manager ?x hpi:isManagedBy ?y .}


http://hpi-web.de/swt1213#


http://www.w3.org/2000/01/rdf-schema#



18 SPARQL Query Format• Search all lectures and their managers ordered by managers in

descending order and limit the results to the first 10 starting the list at position 10:

PREFIX hpi: <http://hpi-web.de/swt1213#>PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>SELECT ?lecture ?managerFROM <...>WHERE { ?x rdf:type hpi:Lecture ; rdfs:label ?lecture . ?y rdf:type hpi:Staff ; rdfs:label ?manager ?x hpi:isManagedBy ?y .}ORDER BY DESC (?manager)LIMIT 10OFFSET 10







19

Blank Nodes in SPARQL Queries• as subject or object of a triple pattern• „non selectable“ variables

• blank node identifier might also occur in the results

PREFIX hpi: <http://hpi-web.de/swt1213#>PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>SELECT ?nameWHERE { _:x rdf:type hpi:Lecture ; hpi:isManagedBy [ rdfs:label ?name] .}


http://hpi-web.de/WS0910#

http://hpi-web.de/WS0910#




20

How should I know all those prefixes?



21 SPARQL Query Format• The keyword FILTER specifies constraints for the results

• FILTER expressionscontain operatorsand functions

• FILTERs can NOT assign/create new values

# Default Graph (stored at http://example.org/book) @prefix dc: <http://purl.org/dc/elements/1.1/> .@prefix : <http://example.org/book/> .@prefix ns: <http://example.org/ns#> .

:book1 dc:title "SPARQL Tutorial" .:book1 ns:price 42 .:book2 dc:title "The Semantic Web" .:book2 ns:price 23 .

PREFIX dc: <http://purl.org/dc/elements/1.1/>PREFIX ns: <http://example.org/ns#>SELECT ?title ?priceFROM <http://example.org/book> WHERE { ?x ns:price ?price . FILTER (?price < 30.5) ?x dc:title ?title . }


http://example.org/book.rdf


http://example.org/book

http://example.org/book


22 SPARQL Unary Operators in Constraints

Operator Type(A) Result Type!A xsd:boolean xsd:boolean

+A numeric numeric

-a numeric numeric

BOUND(A) variable xsd:boolean

isURI(A) RDF term xsd:boolean

isBLANK(A) RDF term xsd:boolean

isLITERAL(A) RDF term xsd:boolean

STR(A) literal/URI simple literal

LANG(A) literal simple literal

DATATYPE(A) literal URI



23 More SPARQL Operators

• Logical connectives && and || for xsd:boolean• Comparison operators =, !=, <, >, <=, and >= for numeric

datatypes, xsd:dateTime, xsd:string, and xsd:boolean• Comparison operators = and != for other datatypes• Arithmetic operators +, -, *, and / for numeric datatypes

• and in addition:• REGEX(String,Pattern) or REGEX(String,Pattern,Flags)• sameTERM(A,B) • langMATCHES(A,B)



24 Filter Constraints are Evaluated in 3-valued Logic• true, false, and error

A !AT FF TE E

A B A || B A && B

T T T T

T F T F

F T T F

F F F F

T E T E

E T T E

F E E F

E F E F

E E E E



25 SPARQL Query Format• The keyword OPTIONAL selects optional elements from the RDF graph• complies to a Left Outer Join

# Default Graph (stored at http://example.org/addresses) @prefix foaf: <http://xmlns.com/foaf/0.1/> .@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

_:a rdf:type foaf:Person ._:a foaf:name "Alice" ._:a foaf:mbox <mailto:[email protected]> ._:a foaf:mbox <mailto:[email protected]> .

_:b rdf:type foaf:Person ._:b foaf:name "Bob" .

PREFIX foaf: <http://xmlns.com/foaf/0.1/>SELECT ?name ?mboxFROM <http://example.org/addresses>WHERE { ?x foaf:name ?name . OPTIONAL { ?x foaf:mbox ?mbox }}




http://example.org/addressed.rdf

http://example.org/addressed.rdf


26

SPARQL Query Format• The keyword UNION allows for alternatives (logical disjunction)

# Default Graph (stored at http://example.org/book) @prefix dc10: <http://purl.org/dc/elements/1.0/> .@prefix dc11: <http://purl.org/dc/elements/1.1/> .

_:a dc10:title "SPARQL Query Language Tutorial" ._:a dc10:creator "Alice" .

_:b dc11:title "SPARQL Protocol Tutorial" ._:b dc11:creator "Bob" .

_:c dc10:title "SPARQL" ._:c dc11:title "SPARQL (updated)" .

PREFIX dc10: <http://purl.org/dc/elements/1.0/>PREFIX dc11: <http://purl.org/dc/elements/1.1/>

SELECT ?titleFROM <http://example.org/books>WHERE { { ?book dc10:title ?title } UNION { ?book dc11:title ?title } }




http://example.org/books

http://example.org/books


27 SPARQL Query Format• Negation in SPARQL• (complies to ,NOT EXISTS‘ in SQL)

@prefix foaf: <http://xmlns.com/foaf/0.1/> .@prefix dc: <http://purl.org/dc/elements/1.1/> .@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

_:a foaf:givenName "Alice".

_:b foaf:givenName "Bob" ._:b dc:date "2005-04-04T04:04:04Z"^^xsd:dateTime .

PREFIX foaf: <http://xmlns.com/foaf/0.1/>PREFIX dc: <http://purl.org/dc/elements/1.1/>SELECT ?name WHERE { ?x foaf:givenName ?name . OPTIONAL { ?x dc:date ?date } . FILTER (!bound(?date)) }


http://xmlns.com/foaf/0.1/


http://purl.org/dc/elements/1.1/


http://www.w3.org/2001/XMLSchema#

http://www.w3.org/2001/XMLSchema#






28 SPARQL Query Format•SPARQL queries are executed over an RDF dataset•one (or more) default RDF graph•zero or more named RDF graphs

•Named Graphs can explicitely addressed via keyword GRAPH and the URI of the named graph

GRAPH <http://example.org/graph1.rdf> { ?x foaf:mbox ?mbox }



29PREFIX hpi: ...SELECT ...FROM hpi:g1FROM hpi:g4FROM NAMED hpi:g1FROM NAMED hpi:g2FROM NAMED hpi:g3WHERE { ... A ... GRAPH hpi:g3 { ... B ... } GRAPH ?g { ... C ... }}

hpi:g1

hpi:g4

Default Graph

hpi:g1

hpi:g2

hpi:g3

Default Graph

Named Graphs



30 SPARQL Query Format• Example for Named Graphs

@prefix dc: <http://purl.org/dc/elements/1.1/> .

<http://example.org/bob> dc:publisher "Bob Hacker" .<http://example.org/alice> dc:publisher "Alice Hacker" .

Default Graph (stored at http://example.org/dft.ttl)

@prefix foaf: <http://xmlns.com/foaf/0.1/> .

_:a foaf:name "Bob" ._:a foaf:mbox <mailto:[email protected]> .

Named Graph: http://example.org/bob

@prefix foaf: <http://xmlns.com/foaf/0.1/> .

_:a foaf:name "Alice" ._:a foaf:mbox <mailto:[email protected]> .

Named Graph: http://example.org/alice


http://example.org/dft.ttl

http://example.org/dft.ttl


31 SPARQL Query Format• Example for Named Graphs

PREFIX foaf: <http://xmlns.com/foaf/0.1/>PREFIX dc: <http://purl.org/dc/elements/1.1/>

SELECT ?who ?g ?mboxFROM <http://example.org/dft.ttl>FROM NAMED <http://example.org/alice>FROM NAMED <http://example.org/bob>WHERE{ ?g dc:publisher ?who . GRAPH ?g { ?x foaf:mbox ?mbox }}

Default Graph

Named Graph



32 SPARQL Result Format• SPARQL results are given as well formed and valid XML documents

• In a <head> element all variables of the SPARQL query are listed

<?xml version="1.0"?><sparql xmlns="http://www.w3.org/2005/sparql-results#"> ...</sparql>

<head> <variable name="x"/> <variable name="hpage"/> <variable name="name"/> <variable name="mbox"/> <variable name="blurb"/> </head>



33 SPARQL Result Format• For each SPARQL Query result exists a <result> element

<?xml version="1.0"?><sparql xmlns="http://www.w3.org/2005/sparql-results#"> <head> <variable name="x"/> ... </head> <results> <result> <binding name="x"> ... </binding> <binding name="hpage"> ... </binding> </result>

<result> ... </result> ... </results></sparql>



34 SPARQL Result Format• Within a <binding> element a <head> variable is bound to a result

<result> <binding name="x"> <bnode>r2</bnode> </binding> <binding name="hpage"> <uri>http://work.example.org/bob/</uri> </binding> <binding name="name"> <literal xml:lang="en">Bob</literal> </binding> <binding name="age"> <literal datatype="http://www.w3.org/2001/XMLSchema#integer">30</literal> </binding> <binding name="mbox"> <uri>mailto:[email protected]</uri> </binding> </result>



35 SPARQL Query Format (continued)• In addition to SELECT queries SPARQL allows:

•ASK•Check whether there is at least one result •Result: true or false•Result is delivered as XML or JSON

•CONSTRUCT•Result: an RDF graph constructed from a template•Template: graph pattern with variables from the query pattern•Result is RDF/XML or Turtle

•DESCRIBE•Result: an RDF graph with data about resources•Result is RDF/XML or Turtle



36 SPARQL Construction of RDF Graphs• CONSTRUCT defines a template for the construction of new RDF Graphs

@prefix org: <http://example.com/ns#> .

_:a org:employeeName "Alice" ._:a org:employeeId 12345 .

_:b org:employeeName "Bob" ._:b org:employeeId 67890 .

data

PREFIX foaf: <http://xmlns.com/foaf/0.1/>PREFIX org: <http://example.com/ns#>

CONSTRUCT { ?x foaf:name ?name }WHERE { ?x org:employeeName ?name }



37 SPARQL Construction of RDF Graphs• Result of a CONSTRUCT query as serialized RDF/XML

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:foaf="http://xmlns.com/foaf/0.1/"> <rdf:Description> <foaf:name>Alice</foaf:name> </rdf:Description> <rdf:Description> <foaf:name>Bob</foaf:name> </rdf:Description></rdf:RDF>



38 SPARQL Protocol• Method to query/respond of SPARQL queries via http• A SPARQL URI consists out of 3 parts:1.URL of a SPARQL endpoint

(e.g. http://example.org/sparql)2.RDF Graph(s) to be queried

(optional, part of the query string, z.B. named-graph-uri=http://example.org/testrdf.rdf)

3.Query string (part of the query string, e.g. query=SELECT...)

http://example.org/sparql?named-graph-uri=http%3A%2F%2Fexample.org%2Ftestrdf&query=SELECT+%3Freview_graph+WHERE+%7B%0D%0A++GRAPH+%3Freview_graph+%7B%0D%0A+++++%3Freview+rev%3Arating+10+.%0D%0A++%7D%0D%0A%7D


http://example.org/sparql

http://example.org/sparql

http://example.org/testrdf.rdf

http://example.org/testrdf.rdf

http://example.org/sparql?named-graph-uri=http%3A%2F%2Fexample.orgm%2F

http://example.org/sparql?named-graph-uri=http%3A%2F%2Fexample.orgm%2F


39 SPARQL Protocol -- Example• Simple SPARQL Query

• HTTP Trace of the SPARQL Query

PREFIX dc: <http://purl.org/dc/elements/1.1/> SELECT ?book ?who WHERE { ?book dc:creator ?who }

GET /sparql/?query=EncodedQuery&default-graph-uri=http://www.other.example/books HTTP/1.1Host: www.other.exampleUser-agent: my-sparql-client/0.1


http://www.other.example/books





40 • HTTP Trace of the SPARQL Response

HTTP/1.1 200 OKDate: Fri, 06 May 2008 20:55:12 GMTServer: Apache/1.3.29 (Unix) PHP/4.3.4 DAV/1.0.3Connection: closeContent-Type: application/sparql-results+xml<?xml version="1.0"?><sparql xmlns="http://www.w3.org/2005/sparql-results#"> <head> <variable name="book"/> <variable name="who"/> </head> <results ordered="false" distinct="false"> <result> <binding name="who"> <literal>Bob Hacker</literal> </binding> <binding name=“book"> <literal>The Art of Hacking</literal> </binding> ...</sparql>



41 • some popular SPARQL (1.0) Endpoints• http://sparql.org/sparql.html --

General Purpose SPARQL Endpoint• http://dbpedia.org/sparql --

DBpedia SPARQL Endpoint• http://www4.wiwiss.fu-berlin.de/dblp/sparql --

DBLP (Computer Science Bibliographies) SPARQL Endpoint• http://linkedmdb.org/sparql --

Linked Movie Database SPARQL Endpoint• http://www4.wiwiss.fu-berlin.de/factbook/sparql --

CIA World Factbook SPARQL Endpoint• ...


http://sparql.org/sparql.html

http://sparql.org/sparql.html

http://dbpedia.org/sparql

http://dbpedia.org/sparql

http://www4.wiwiss.fu-berlin.de/dblp/sparql

http://www4.wiwiss.fu-berlin.de/dblp/sparql

http://linkedmdb.org/sparql

http://linkedmdb.org/sparql

http://www4.wiwiss.fu-berlin.de/factbook/sparql

http://www4.wiwiss.fu-berlin.de/factbook/sparql


42

SPARQL 1.1 nextAtt

ention

: There

might

occur

chang

es in

syntax

and

semant

ics be

cause

this i

s stil

l a wo

rking

draft.

..



43

SPARQL 1.1 -- New Features• SPARQL 1.1 Query:

• Assignments (e.g. BIND, SELECT expressions) • Aggregate functions (e.g. COUNT, SUM, AVG) • Subqueries• Negation (EXISTS, NOT EXISTS, MINUS) • Property paths• Basic query federation (SERVICE, BINDINGS)

• SPARQL 1.1 Update:• Graph update (INSERT DATA, DELETE DATA, INSERT,

DELETE, DELETE WHERE, LOAD, CLEAR)• Graph management (CREATE, DROP, COPY, MOVE, ADD)

• SPARQL 1.1 Entailment for RDF, RDFS, OWL, RIF• SPARQL 1.1 Service Descriptions



44 SPARQL 1.1 -- Assignments• SPARQL 1.1 allows the creation of new values in a query

PREFIX ex: <http://example.org/> SELECT ?Item (?Pr * 1.1 AS ?NewP )WHERE { ?Item ex:price ?Pr }

@prefix ex: <http://example.org/> .

ex:lemonade1 ex:price 3 .ex:beer1 ex:price 3.ex:wine1 ex:price 3.50 .ex:liqueur1 ex:price "n/a".

?Item ?NewP

lemonade1 3.3

beer1 3.3

wine1 3.85

liqueur1

Data

Result



45 SPARQL 1.1 -- Aggregate Functions• SPARQL 1.1 allows the use of aggregate functions in queries

?C

5

DataResult

PREFIX ex: <http://example.org/> SELECT (Count(?Item) AS ?C)WHERE { ?Item ex:price ?Pr }


ex:lemonade1 ex:price 3 ; rdf:type ex:Softdrink.ex:beer1 ex:price 3; rdf:type ex:Beer.ex:wine1 ex:price 3.50 ; rdf:type ex:Wine.ex:wine2 ex:price 4 . rdf:type ex:Wine.ex:wine3 ex:price "n/a”; rdf:type ex:Wine.




?C

3

DataResult



PREFIX ex: <http://example.org/> SELECT (Count(DISTINCT ?T) AS ?C)WHERE { ?Item rdf:type ?T }




DataResult



PREFIX ex: <http://example.org/> SELECT ?T (Count(?Item) AS ?C)WHERE { ?Item rdf:type ?T }GROUP BY ?T

?T ?C

Softdrink 1

Beer 1

Wine 3




Data Result@prefix ex: <http://example.org/> .


?T ?C

Wine 3

PREFIX ex: <http://example.org/> SELECT ?T (Count(?Item) AS ?C)WHERE { ?Item rdf:type ?T }GROUP BY ?THAVING Count(?Item) > 1



49 SPARQL 1.1 -- Aggregate Functions• more aggregate functions

• SUM• AVG• MIN• MAX• SAMPLE -- „pick“ one non-deterministically• GROUP_CONCAT -- concatenate values with a designated

string separator



50

SPARQL 1.1 -- Aggregate Functions• Example for SAMPLE, GROUP_CONCAT

Result

@prefix ex: <http://example.org/> .@prefix foaf: <http://xmlns.com/foaf/0.1/> .

ex:alice a foaf:Person; foaf:name "Alice Wonderland"; foaf:nick "Alice", "The real Alice".

ex:bob a foaf:Person; foaf:name "Robert Doe", "Robert Charles Doe", "Robert C. Doe"; foaf:nick "Bob","Bobby","RobC","BobDoe".

ex:charles a foaf:Person; foaf:name "Charles Charles"; foaf:nick "Charlie" .

Data

PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ( SAMPLE(?N) as ?Name) ( GROUP_CONCAT(?M; SEPARATOR = ", ") AS ?Nicknames )WHERE { ?P a foaf:Person ; foaf:name ?N ; foaf:nick ?M . }GROUP BY ?P

?Name ?Nicknames

Alice Wonderland The real Alice, Alice

Charles Charles Charlie

Robert C. Doe Bob, BobDoe, RobC, Bobby



51

SPARQL 1.1 -- Subqueries• Subqueries are a way to embed SPARQL queries within other

queries

• returns titles for 10 persons, instead of just 10 rows• result is achieved by first evaluating the inner query

SELECT ?TWHERE { ?D foaf:maker ?P ; rdfs:label ?T . { SELECT DISTINCT ?P WHERE { ?D foaf:maker <http://dblp.l3s.de/…/authors/Harald_Sack>, ?P . FILTER ( ?P != <http://dblp.l3s.de/…/authors/Harald_Sack> ) } LIMIT 10 }}



52

SPARQL 1.1 -- Negation• Filtering of query solutions is done within a FILTER expression

using NOT EXISTS and EXISTS.PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?personWHERE { ?person rdf:type foaf:Person . FILTER NOT EXISTS { ?person foaf:name ?name }}

@prefix : <http://example/> .@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .@prefix foaf: <http://xmlns.com/foaf/0.1/> .

:alice rdf:type foaf:Person .:alice foaf:name "Alice" .:bob rdf:type foaf:Person .

Data

Result

?person

:bob


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




http://example/

http://example/






53

SPARQL 1.1 -- Negation• Filtering of query solutions be removing possible solutions with

MINUS.PREFIX : <http://example/>PREFIX foaf: <http://xmlns.com/foaf/0.1/>SELECT DISTINCT ?sWHERE { ?s ?p ?o . MINUS { ?s foaf:givenName "Bob" . }}

@prefix : <http://example/> .@prefix foaf: <http://xmlns.com/foaf/0.1/> .

:alice foaf:givenName "Alice" ; foaf:familyName "Smith" .

:bob foaf:givenName "Bob" ; foaf:familyName "Jones" .

:carol foaf:givenName "Carol" ; foaf:familyName "Smith" .

Data

Result

?s

:alice

:carol


http://example/

http://example/



http://example/

http://example/




54

SPARQL 1.1 -- Property Paths• A property path is a possible route through an RDF graph

between two graph nodes.• trivial case: property path of length 1, i.e. a triple pattern• alternatives: match one or both possibilities

• sequence: property path of length >1

• inverse property paths: reversing the direction of the triple

{ :book1 dc:title|rdfs:label ?displayString }

{ ?x foaf:mbox <mailto:alice@example> . ?x foaf:knows/foaf:knows/foaf:name ?name . }

{ ?x foaf:mbox <mailto:alice@example> }

=

{ <mailto:alice@example> ^foaf:mbox ?x }


mailto:alice@example

mailto:alice@example


55

SPARQL 1.1 -- Property Paths

• inverse path sequences paths

• arbitrary length match

• negated property paths

{ ?x foaf:knows/^foaf:knows ?y . FILTER(?x != ?y) }

{ ?x foaf:mbox <mailto:alice@example> . ?x foaf:knows+/foaf:name ?name . }

{ ?x !(rdf:type|^rdf:type) ?y }



56




57


• can be used for some ontological inference• e.g. „find all beers in the beer ontology“

PREFIX beer: <http://www.purl.org/net/ontology/beer#>PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT ?beerFROM <http://www.purl.org/net/ontology/beer>WHERE { ?beer rdf:type/rdfs:subClassOf* beer:Beer .}



58

SPARQL 1.1 -- New Features• SPARQL 1.1 Query:

• Assignments (e.g. BIND, SELECT expressions) • Aggregate functions (e.g. COUNT, SUM, AVG) • Subqueries• Negation (EXISTS, NOT EXISTS, MINUS) • Property paths• Basic query federation (SERVICE, BINDINGS)

• SPARQL 1.1 Update:• Graph update (INSERT DATA, DELETE DATA, INSERT,

DELETE, DELETE WHERE, LOAD, CLEAR)• Graph management (CREATE, DROP, COPY, MOVE, ADD)

• SPARQL 1.1 Entailment for RDF, RDFS, OWL, RIF• SPARQL 1.1 Service Descriptions

more to come...



59



• Uniform Resource Identifier - URI• Recapitulation: XML and XMLSchema• Resource Description Framework - RDF

• RDF Schema• How Do We Get Semantics into WWW?

• µFormats, schema.org, RDFa and GRDDL• Query Language SPARQL• Why RDF(S) is not enough

Short Excursion:

Triple Stores



60 RDF in RDBMS• RDF Graph can be represented by a set of Triples (s,p,o)• Triples can simply be stored in a relational database system

• Idea: Use a specific relational schema for RDF data and benefit from 40 years of research in the DB community

• 3 steps for SPARQL query processing:(1) Convert SPARQL query to SQL query (w.r.t. the schema)(2) Use RDBMS to answer SQL query(3) Generate SPARQL query result from SQL query result

How to store RDF Data?



61 • RDF Graph can be represented by a set of Triples (s,p,o)• Triples can simply be stored in a relational database

management system (RDBMS)

• Motivation: Use a specific relational schema for RDF data and benefit from 40 years of research in the DB community

• 3 steps for SPARQL query processing:(1) Convert SPARQL query to SQL query

(w.r.t. the schema)(2) Use RDBMS to answer SQL query(3) Generate SPARQL query result from SQL query result

RDF in RDBMS



62

Problem

• How to store RDF triples to carry out efficient SPARQLqueries?

• 4 alternatives:1.Giant Triple Storage2.Property Tables3.Vertically Partitioned Tables (Binary Tables)4.Hexastore



63

• Basic idea:• Store all RDF triples in a single table• performance depends on efficient indexing

Pros:• easy to implement• works for huge numbers of properties,

if indexes are chosen with care

Cons:• many self joins

Giant Triple Storage



64

SELECT ?university WHERE { ?v rdf:type :GradStudent; :bachelorsFrom ?university . }

SELECT t2.o AS universityFROM triples AS t1, triples AS t2WHERE t1.p=‘type‘ AND t1.o=‘GradStudent‘ AND t2.p=‘bachelorsFrom‘ AND t1.s=t2.s

SPARQL

SQL

Giant Triple Storage

• Basic idea:• Store all RDF triples in a single table• performance depends on efficient indexing



65 • Use numerical identifier for each RDF term in the dataset• saves space and enhances efficiency

RDF Term ID:ID1 1

rdf:type 2:FullProfessor 3

:teacherOf 4“AI“ 5

:bachelorsFrom 6“MIT“ 7

:mastersFrom 8“Cambridge 9

s p o

1 2 31 4 51 6 71 8 91 10 11

12 13 1412 15 712 4 16

ID Based Triple Storage



66 • Storing multiple RDF graphs• used for provenance, versioning, contexts, etc.

RDF Term ID:ID1 1

rdf:type 2:FullProfessor 3

:teacherOf 4“AI“ 5

:bachelorsFrom 6“MIT“ 7

:mastersFrom 8“Cambridge 9

g s p o

100 1 2 3100 1 4 5101 12 16 7100 1 8 9102 23 21 11100 1 6 7102 23 22 8101 12 4 16

Quad Tables



67 • Combining all (or some) properties of similar subjects in n-ary tables

• Use ID based encoding for efficiency

Pros:• Fewer joins• If the data is structured, we

have a relational DB

Cons:• Potentially a lot of NULLs• Clustering is not trivial• Multi-value properties are

complicated

Property Tables



68 • for each unique property create a two column table• Use ID based encoding for efficiency

Pros:• Supports multi-value properties• No NULLs• Read only needed attributes

(i.e. less I/O)• No clustering• Excellent performance (if number

of properties is small, queries with bounded properties)

Cons:• Expensive inserts• Bad performance (large number

of properties, queries with unbounded properties)

Vertically Partitioned Tables (Binary Tables)



69

Olaf Hartig - Linked Data Management and SPARQL Querying 72

Row Store vs. Column Store

● Different physical storage models for relational DBs

● Row based storage:

● Tuples (i.e. DB records)are stored consecutively

● Entire row needs to beread even if few attributesare projected

● Column based storage:

● Read columns relevant to thequery → projection is free

● Inserts are expensive

● Column based storage model fit binary tables very well

1 2

3

1 2

3

1 2

3

• Different physical storage models for relational DBs

• Row Based Storage• Tuples (i.e. DB records) are stored consecutively• Entire row needs to be read even if few

attributes are projected

• Column Based Storage• Read columns relevant to the query → projection is free

• Inserts are expensive

Vertically Partitioned Tables (Binary Tables)



70 • Create an index for every possible combination to enable efficient processing• spo, pos, osp, sop, pso, ops

Hexastores



71

Pros:• fast joins (in the beginning)

Cons:• 5 times more storage• weak performance, when

disk access is necessary

Hexastores

• Create an index for every possible combination to enable efficient processing• spo, pos, osp, sop, pso, ops



72





Vorlesung Semantic Web Technologien, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam

73

2. Semantic Web Basisarchitektur2.7 Warum RDF noch nicht ausreicht

Is there more in the World than RDF(S)?



74 Locality of global properties

Problem: Cows only eat vegetablesOther animals also eat meat.

2.7 Why RDF(S) is not enough

Animal eats FoodDomain Range

Vegetables

Meat

subClassOf

subClassOf



75Disjunctive Classes

Problem: Subclass relation cannot express disjunctive class (subclass) membership


Human

Woman

Man

subClassOf

subClassOf

≠



76Class Combinations

Problem: Combination of classes define a new class.New class contains only members from given class combinations.


Motorist

Pedestrian

Motorcyclist

Cyclist

Road User

subClassOf

subClassOf

subClassOf

subClassOf



77Cardinality Restrictions

Problem: Every human (usually) has two partents


Human ParenthasParent



78 Special Property Constraints

• Transitivity (e.g. „is greater than“)• Uniqueness (e.g. „is mother of“) • Inversiveness (e.g. „is parent of“ and „is child of“)

We need more se

mantic express

ivity!




79




80






81

Ontolo

gies

next Lecture:



82

Bibliography

• P. Hitzler, S. Roschke, Y. Sure: Semantic Web Grundlagen, Springer, 2007.

• P. Hitzler, M. Krötzsch, S. Rudolph:Foundations of Semantic Web Technologies,CRC Press, 2009.

2. Semantic Web Basic Architecture2.6 Query Language SPARQL2.7. Why RDF(S) is not enough


http://www.amazon.de/gp/product/3540339930/ref=as_li_ss_tl?ie=UTF8&tag=moresemantic-21&linkCode=as2&camp=1638&creative=19454&creativeASIN=3540339930


http://www.amazon.de/gp/product/142009050X/ref=as_li_ss_tl?ie=UTF8&tag=moresemantic-21&linkCode=as2&camp=1638&creative=19454&creativeASIN=142009050X

http://www.amazon.de/gp/product/142009050X/ref=as_li_ss_tl?ie=UTF8&tag=moresemantic-21&linkCode=as2&camp=1638&creative=19454&creativeASIN=142009050X


83

• B. du Charme: Learning SPARQL,O‘Reilly Media, 2011.

Acknowledgements:Thanks a lot to Olaf Hartig and Axel Polleres, who I stole some examples from...





Lecture Semantic Web Technologies, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam

84

□Bloghttp://semweb2013.blogspot.com/

□Webseitehttp://www.hpi.uni-potsdam.de/studium/lehrangebot/itse/veranstaltung/semantic_web_technologien-3.html

□bibsonomy - Bookmarkshttp://www.bibsonomy.org/user/lysander07/swt1213_04





http://www.hpi.uni-potsdam.de/studium/lehrangebot/itse/veranstaltung/semantic_web_technologien-3.html






http://www.bibsonomy.org/user/lysander07/swt1213_04




Education

(04) Semantic Web Technologies - SPARQL