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Usage of Linked Data
Introduction and Application Scenarios
Presented by:
Barry Norton
Agenda
1. Motivation Scenario
2. Linked Data Foundations
3. Introduction to Linked Data
4. Linked Data use case scenarios
2
MOTIVATION SCENARIO
3
Music!
4
• Provision of a music-based portal.
• Bring together a number of disparate components of
data-oriented content:
1. Musical content (streaming data & downloads)
2. Music and artist metadata
3. Review content
4. Visual content (pictures of artists & albums)
Music!
5
Visualization
Module
Metadata
Streaming providers
Physical Wrapper
Downloads
Dataacquisition
D2R Transf.LD Wrapper
Musical Content
Application
Analysis &
Mining Module
LDDatasetAccess
LD Wrapper
RDF/
XML
Integrated
Dataset
Interlinking Cleansing
Vocabulary
Mapping
SPARQL
Endpoint
Publishing
RDFa
Other content
Music!
6
Expected Results
• The developer will contribute back the aggregated and
interlinked content to the Linked Open Data Cloud.
• Linking of artists will be improved.
• Metadata, visual content and reviews will be improved.
• Links to emerging Web technologies that inherit from
semantics: Google RichSnippets, Facebook OpenGraph
and schema.org annotation.
LINKED DATA FOUNDATIONS
7
• Extension of the technology of computer
networks.
• The technology supporting the Internet
includes the Internet Protocol (IP) .
• Each computer on the Internet is assigned an
IP number.
• Messages can be routed from one computer
to another.
Internet
8
Internet
9
Source: http://www.evolutionoftheweb.com
The growth of the Internet
• There is a wealth of information on the Web.
• It is aimed mostly towards consumption by
humans as end-users:
• Recognize the meaning behind content and draw conclusions,
• Infer new knowledge using context and
• Understand background information.
TheWeb
10
TheWeb
11
• Billions of diverse documents online, but it is not easily
possible to automatically:
• Retrieve relevant documents.
• Extract information.
• Combine information in a meaningful way.
• Idea:
• Also publish machine processable data on the web.
• Formulate questions in terms understandable for a machine.
• Do this in a standardized way so machines can interoperate.
• The Web becomes a Web of Data
• This provides a common framework to share knowledge on the Web
across application boundaries.
TheWeb: Evolution
12
Web of Documents Web of Data
"Documents"
Hyperlinks Typed Links
"Things"
TheWeb: Evolution
13
Source: http://www.radarnetworks.com
WebTechnology Basics
14
HTML – HyperText Markup Language
• Language for displaying web pages and other
information in a web browser.
• HTML elements consist of tags (enclosed in angle
brackets), attributes and content.
HTTP – HypertextTransfer Protocol
• Foundation of data communication for the WWW.
• Client-server protocol.
• Every interaction is based on: request and response.
WebTechnology Basics
15
Uniform Resource Identifier (URI)
• Compact sequence of characters that identifies an
abstract or physical resource.
• Examples:
ldap://[2001:db8::7]/c=GB?objectClass?one
mailto:John.Doe@example.com
news:comp.infosystems.www.servers.unix
tel:+1-816-555-1212
telnet://192.0.2.16:80/
urn:oasis:names:specification:docbook:dtd:xml:4.1.2
http://dbpedia.org/resource/Karlsruhe
Describing Data
16
Vocabularies
• Collections of defined relationships and classes of
resources.
• Classes group together similar resources.
• Terms from well-known vocabularies should be
reused wherever possible.
• New terms should be define only if you can not find
required terms in existing vocabularies.
Describing Data
17
Vocabularies
A set of well-known vocabularies has evolved in the
Semantic Web community. Some of them are:
Vocabulary Description Classes and Relationships
Friend-of-a-Friend (FOAF) Vocabulary for describing
people.
foaf:Person, foaf:Agent, foaf:name,
foaf:knows, foaf:member
Dublin Core (DC) Defines general metadata
attributes.
dc:FileFormat, dc:MediaType,
dc:creator, dc:description
Semantically-Interlinked
Online Communities (SIOC)
Vocabulary for representing
online communities.
sioc:Community, sioc:Forum,
sioc:Post, sioc:follows, sioc:topic
Music Ontology (MO) Provides terms for describing
artists, albums and tracks.
mo:MusicArtist, mo:MusicGroup,
mo:Signal, mo:member, mo:record
Simple Knowledge
Organization System (SKOS)
Vocabulary for representing
taxonomies and loosely
structured knowledge.
skos:Concept, skos:inScheme,
skos:definition, skos:example
Describing Data
18
Vocabularies
More extensive lists of well-known vocabularies are
maintained by:
• W3C SWEO Linking Open Data community project
http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/CommonVocabularies
• Mondeca: Linked Open Vocabularies
http://labs.mondeca.com/dataset/lov
• Library Linked Data Incubator Group: Vocabularies in
the library domain
http://www.w3.org/2005/Incubator/lld/XGR-lld-vocabdataset-20111025
Semantics on theWeb
19
Semantic Web Stack
Berners-Lee (2006)
Semantics on theWeb
20
Semantic Web Stack
Berners-Lee (2006)
Syntatic basis
Basic data model
Simple vocabulary
(schema) language
Expressive vocabulary
(ontology) language
Query language
Application specific
declarative-knowledge
Digital signatures,
recommendations
Proof generation,
exchange, validation
Semantics on theWeb
21
Semantic Web Stack
Berners-Lee (2006)
RDF – Resource Description Framework
Semantics on theWeb
22
RDF – Resource Description Framework
• RDF is the basis layer of the Semantic Web stack
‘layer cake’.
• Basic building block: RDF triple.
• Subject – a resource, which may be identified with a URI.
• Predicate – a URI-identified reused specification of the
relationship.
• Object – a resource or literal to which the subject is related.
Semantics on theWeb
23
RDF – Resource Description Framework
(Example)
<http://musicbrainz.org/artist/b10bbbfc-
cf9e-42e0-be17-e2c3e1d2600d#_>
<http://www.w3.org/2002/07/owl#sameAs>
<http://dbpedia.org/resource/The_Beatles>.
<http://musicbrainz.org/artist/b10bbbfc-
cf9e-42e0-be17-e2c3e1d2600d#_>
<http://xmlns.com/foaf/0.1/name>
"The Beatles" .
URIs are given in
angle brackets in
N-Triples.
Literals are given in quotes in N-Triples.
In N-Triples every
statement is
terminated with a
full stop.
Semantics on theWeb
24
RDF Graphs
• Every set of RDF assertions can then be drawn and
manipulated as a (labelled directed) graph:
• Resources – the subjects and objects are nodes of the
graph.
• Predicates – each predicate use becomes a label for an arc,
connecting the subject to the object.
Subject Object
Predicate
RDF Graphs (Example)
25
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
<http://www.w3.org/2002/07/owl#sameAs>
<http://xmlns.com/foaf/0.1/name>
<http://dbpedia.org/resource/The_Beatles> "The Beatles"
Semantics on theWeb
Semantics on theWeb
26
RDF Blank Nodes
• RDF graphs can also contain unidentified resources, called
blank nodes:
• Blank nodes can group related information, but their use in
Linked Data is discouraged.
[] <http://www.w3.org/2003/01
/geo/wgs84_pos#Point>
<http://www.w3.org/1999/02/
22-rdf-syntax-ns#type>
<http://www.w3.org/2003/01
/geo/wgs84_pos#lat>
<http://www.w3.org/2003/01
/geo/wgs84_pos#long>
49.005 8.386
Semantics on theWeb
27
RDFTurtle
• Turtle is a syntax for RDF more readable.
• Since many URIs share same basis we use prefixes:
@prefix rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>.
@prefix rdfs:<http://www.w3.org/2000/01/rdf-schema#>.
@prefix owl:<http://www.w3.org/2002/07/owl#>.
@prefix mo:<http://purl.org/ontology/mo/>.
@prefix dbpedia:<http://dbpedia.org/resouce/>.
And (sometimes) a unique base:
@base <http://musicbrainz.org/>.
Semantics on theWeb
28
RDFTurtle
• Also has a simple shorthand for class membership:
@base <http://musicbrainz.org/>.
@prefix mo:<http://purl.org/ontology/mo/>.
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> a mo:MusicGroup.
Is equivalent to:
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://purl.org/ontology/mo/MusicGroup>.
RDFTurtle
• When multiple statements apply to same subject they
can be abbreviated as follows:
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
rdfs:label "The Beatles";
owl:sameAs dbpedia:The_Beatles ,
<http://www.bbc.co.uk/music/artists/
b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#artist> .
Same subject &
predicate
Semantics on theWeb
29
Same subject
RDFTurtle
• Turtle also provides a simple syntax for datatypes and
language tags for literals, respectively:
<recording/5098d0a8-d3c3-424e-9367-1f2610724410#_> a mo:Signal;
rdfs:label "All You Need Is Love" ;
mo:duration "PT3M48S"^^xsd:duration .
dbpedia:The_Beatles dbpedia-owl:abstract
"The Beatles were an English rock band formed (…) "@en,
"The Beatles waren eine britische Rockband in den (…) "@de .
Semantics on theWeb
30
Semantics on theWeb
31
RDF/XML
• This is most useful for inter-machine communication.
• The primary (recurring) element in encoding
assertions (thereby triples) is rdf:Description, e.g.:
<rdf:Description
rdf:about="http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_">
<foaf:name>The Beatles</foaf:name>
<owl:sameAs rdf:resource="http://dbpedia.org/resource/The_Beatles">
</rdf:Description>
<rdf:Description
rdf:about="http://musicbrainz.org/artist/4d5447d7-c61c-4120-ba1b-d7f471d385b9#_">
<foaf:name>John Lennon</foaf:name>
</rdf:Description>
Semantics on theWeb
32
Semantic Web Stack
Berners-Lee (2006)
RDF-S – RDF Schema
Semantics on theWeb
33
RDF-S – RDF Schema
Language for two tasks w.r.t. the RDF data model:
• Expectation – nominate:
• the ‘types’, i.e., classes, of things we might make
assertions about, and
• the properties we might apply, as predicates in these
assertions, to capture their relationships.
• Inference – given a set of assertions, using these classes
and properties, specify what should be inferred about
assertions that are implicitly made.
Semantics on theWeb
34
RDF-S – RDF Schema
• rdf:Property - Class of RDF properties. Example:
mo:member - Indicates a member of a musical group.
• rdfs:domain - States that any resource that has a given
property is an instance of one or more classes.
mo:member rdfs:domain mo:MusicGroup .
• rdfs:range - States that the values of a property are
instances of one or more classes.
mo:member rdfs:range foaf:Agent .
Semantics on theWeb
35
RDF-S – RDF Schema
mo:MusicGroup
rdfs:subClassOf
foaf:Group .
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
rdf:type
mo:MusicGroup .
<artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
rdf:type
foaf:Group .
Schema
Existing
fact
Inferred
fact
We expect to use this
vocabulary to make assertions
about music groups.
Having made such an
assertion...
Inferences can be drawn that
we did not explicitly make
Semantics on theWeb
36
RDF-S – RDF Schema
Resources and predicates
with (limited) inferences:
rdfs:Resource
rdfs:Literal, rdfs:Datatype
rdfs:Class, rdfs:subClassOf
rdfs:subPropertyOf
rdfs:range, rdfs:domain
Some predicates with
NO inferences:
rdfs:comment
rdfs:label
rdfs:seeAlso
rdfs:isDefinedBy
rdf:Property (an instance of rdfs:Class)
Semantics on theWeb
37
Semantic Web Stack
Berners-Lee (2006)
OWL – Web Ontology Language
Semantics on theWeb
38
OWL – Web Ontology Language
• RDFS provides a simplified ontological language for
defining vocabularies about specific domains.
• Sometimes it is necessary to have access to a wider
range of ontological constructs.
• Web Ontology Language (OWL) provides more
ontological constructs and avoids some of the
potential confusion in RDF-S.
Semantics on theWeb
39
OWL 2.0 – Web Ontology Language 2.0
Extends the DL further, but has three more computable fragments
(profiles). OWL 2 Full
• Used informally to refer to RDF graphs considered
as OWL 2 ontologies and interpreted using the
RDF-Based Semantics.
OWL 2 DL
• Used informally to refer to OWL 2 DL ontologies
interpreted using the Direct Semantics.
OWL 2 EL
• Limited to basic classification, but with
polynomial-time reasoning.
OWL 2 QL
• Designed to be translatable to relational database
querying.
OWL 2 RL
• Designed to be efficiently implementable in rule-
based systems.
OWL 2 Full
OWL 2 DL
OWL 2 EL
OWL 2 QL OWL 2 RL
Semantics on theWeb
40
OWL – Web Ontology Language
OWL is made up of terms which provide for:
• Class construction: forming new classes from membership of existing
ones (e.g., unionOf, intersectionOf, etc.).
• Property construction: distinction between OWL ObjectProperties
(resources as values) and OWL DatatypeProperties (literals as values).
• Class axioms: sub-class, equivalence and disjointness relationships.
• Property axioms: sub-property relationship, equivalence and
disjointness, and relationships between properties.
• Individual axioms: statements about individuals (sameIndividual,
differentIndividuals).
Semantics on theWeb
41
Semantic Web Stack
Berners-Lee (2006)
SPARQL – * Protocol and RDF Query Language
Semantics on theWeb
42
SPARQL – * Protocol and RDF Query Language
• Query language designed to use a syntax similar to
SQL for retrieving data from relational databases.
• Different query forms:
• SELECT returns variables and their bindings directly.
• CONSTRUCT returns a single RDF graph specified by a
graph template.
• ASK test whether or not a query pattern has a solution.
Returns yes/no.
• DESCRIBE returns a single RDF graph containing RDF
data about resources.
Semantics on theWeb
43
SPARQL – * Protocol and RDF Query Language
• The syntax of a SELECT query is as follows:
– SELECT nominates which components of the matches
made against the data should be returned.
– FROM (optional) indicates the sources for the data against
which to find matches.
– WHERE defines patterns to match against the data.
– ORDER BY defines a means to order the selected
matches.
Semantics on theWeb
44
SPARQL – * Protocol and RDF Query Language
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX music-ont: <http://purl.org/ontology/mo/>
SELECT ?album_name ?track_title
WHERE {
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
foaf:made ?album .
?album dc:title ?album_name ;
music-ont:track ?track .
?track dc:title ?track_title . }
Retrieve the names of the albums and tracks recorded byThe Beatles.
Semantics on theWeb
45
SPARQL – * Protocol and RDF Query Language
SQL SPARQL
Based on relations (tables). Based on labelled directed graphs.
The relations (tables) to be
matched over should be
indicated.
Assumes a default graph.
(The FROM clause populates this
with specific identified
subgraphs).
(Retrieval) queries produce a
relation from a relation.
SPARQL SELECT queries produce a
relation from a graph.
CONSTRUCT queries (considered
later) produce a graph from a
graph.
Semantics on theWeb
46
SPARQL – * Protocol and RDF Query Language
• SPARQL 1.1 provides graph update operations:
• INSERT DATA: adds explicit triples, given inline.
• DELETE DATA: removes explicit triples, given inline.
• DELETE/INSERT WHERE: updates based on triples calculated
from WHERE clause (as in SELECT and CONSTRUCT).
• LOAD: reads the content of a document into a graph.
• COPY/MOVE/APPEND: manipulates at named graph level.
• CLEAR/DROP: removes all triples in one or more graph.
Semantics on theWeb
47
SPARQL – * Protocol and RDF Query Language
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
INSERT DATA { GRAPH <http://myFavGroups/The_Beatles> {
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_>
foaf:made <http://musicbrainz.org/release/3a685770-7326-34fc-9f18-e5f5626f3dc5#_> ,
< http://musicbrainz.org/release/cb6f8798-d51e-4fa5-a4d1-2c0602bfe1b6#_> .
<http://musicbrainz.org/release/3a685770-7326-34fc-9f18-e5f5626f3dc5#_>
dc:title "Please Please Me".
< http://musicbrainz.org/release/cb6f8798-d51e-4fa5-a4d1-2c0602bfe1b6#_ >
dc:title "Something New". } }
Insert the following albums recorded byThe Beatles into the graph
http://myFavGroups/The_Beatles
Semantics on theWeb
48
SPARQL – * Protocol and RDF Query Language
PREFIX dc: <http://purl.org/dc/elements/1.1/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
DELETE { ?album ?predicate ?object . }
WHERE {
<http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d>
foaf:made ?album .
?album dc:title "Casualities";
?predicate ?object .}
Delete all the information about the album Casualities of The Beatles.
INTRODUCTIONTO:
LINKED DATA
49
Linked Data
50
• Set of best practices for publishing data
on the Web.
• Data from different knowledge
domains, self-described,
linked and accessible.
• Follows 4 simple principles…
Linked Data Principles
1. Use URIs as names for things.
2. Use HTTP URIs so that users can look up
those names.
3. When someone looks up a URI, provide
useful information, using the standards
(RDF*, SPARQL).
4. Include links to other URIs, so that users
can discover more things.
51
Linked Data Principles
• A foundational issue in Linked Data was the
distinction of URIs for object documents that might
describe them.
52
1. Use URIs as names for things.
Linked Data Principles
• HTTP allows a second way to distinguish real-world
objects from documents.
• Best practice says HTTP 303 and Location header
should be used.
53
2. Use HTTP URIs so user can look up those names.
• While RDF/XML should be the default for look-up.
• RDFa annotations in HTML are now also standard.
• SPARQL endpoint for queries are encouraged, or a
dump of the whole dataset.
Linked Data Principles
54
3. When someone looks up a URI, provide useful
information, using the standards (RDF*, SPARQL,
Turtle1).
1 To become a standard.
What to return for a URI?
• Immediate description: triples where the URI is the subject.
• Backlinks: triples where the URI is the object.
• Related descriptions: information of interest in typical usage scenarios.
• Metadata: information as author and licensing information.
• Syntax: RDF descriptions as RDF/XML and human-readable formats.
Linked Data Principles
55
3. When someone looks up a URI, provide useful
information, using the standards (RDF*, SPARQL,
Turtle1).
Source: How to Publish Linked Data on The Web - Chris Bizer, Richard Cyganiak, Tom Heath.
There are several ways to reuse URIs:
• direct reuse
• (OWL) sameAs
• (RDFS) seeAlso
• direct reuse of class/property
• (RDFS) sub-class/-property
• (OWL) equivalent class/property
• SKOS broad match
Linked Data Principles
56
4. Include links to other URIs, so that users can
discover more things.
Instance Level
Schema Level
Linked Data 5 Star
57
Data is available on the Web.
Data is available as machine-readable structured data.
Non-propietary formats are used.
Individual data identified with open standards.
Data is linked to other data provider.
Linked Data 5 Star
58
Example:
"John Lennon"
"Paul McCartney"
"George Harrison"
"Ringo Starr"
My Data
Linked Data 5 Star
59
Data is available on the Web
"John Lennon"
"Paul McCartney"
"George Harrison"
"Ringo Starr"
My Data
It can be retrieved using HTTP.
Linked Data 5 Star
60
Data is available as machine-readable structured data
"John Lennon"
"Paul McCartney"
"George Harrison"
"Ringo Starr"
My Data
"The Beatles"
http://upload.wikimedia.org/wi
kipedia/commons/thumb/d/df/
The_Fabs.JPG/600px-
The_Fabs.JPG
Please Please Me – 1963
A Hard Day‘s Night – 1964
Help! – 1965
Revolver – 1966
…
Images
Scanned Information
Plain text or …
(to continue on the next slide)
Machine-readable data:
Non-propietary formats are used
<schema "http://www.example.com/2012/XMLMyMusic"
version= "1.0" >
<band>
<name>The Beatles</name>
<member>John Lennon</member>
<member>Paul McCartney</member>
<member>George Harrison</member>
<member>Ringo Starr</member>
<picture>http://upload.wikimedia.org/wikipedia/common
s/thumb/d/df/The_Fabs.JPG/600px-
The_Fabs.JPG</picture>
<album year=1963>Please Please Me</album>
<album year=1964>A Hard Day‘s Night</album>
<album year=1965>Help!</album>
<album year=1966>Revolver</album>
…
</band>
Linked Data 5 Star
61
My Data
Linked Data 5 Star
62
Individual data identified with open standards
<schema
"http://www.example.com/2012/XMLMyMusic"
version= "1.0" >
<band>
<name>http://musicbrainz.org/artist/b10bbbfc-cf9e-
42e0-be17-e2c3e1d2600d</name>
<member>http://musicbrainz.org/artist/4d5447d7-
c61c-4120-ba1b-d7f471d385b9</member>
...
<album
year=1963>http://musicbrainz.org/release/5f3ba07b-
4a24-4cd5-b8ad-95ba0fcebec1</album>
…
</band>
My Data
In this context, "Revolver" is an
album! Not a gun.
URI: Uniform Resource Identifier
• Data is uniquely identified
The Beatles
John Lennon
Revolver
• Dissambiguation
Linked Data 5 Star
63
Data is linked to other data provider
<schema
"http://www.example.com/2012/XMLMyMusic"
version= "1.0" >
<band>
<name>http://musicbrainz.org/artist/b10bbbfc-cf9e-
42e0-be17-e2c3e1d2600d</name>
<member>http://musicbrainz.org/artist/4d5447d7-
c61c-4120-ba1b-d7f471d385b9</member>
...
<album
year=1963>http://musicbrainz.org/release/5f3ba07b-
4a24-4cd5-b8ad-95ba0fcebec1</album>
…
<seeAlso>http://dbpedia.org/resource/The_Beatles
</seeAlso>
</band>
http://dbpedia.org/resource/
The_Beatles
Linked Data Cloud
64
2007
Linked Data Cloud
65
2008
Linked Data Cloud
66
2009
Linked Data Cloud
67
2010
Linked Data Cloud
68
2011
Linked Data Cloud
69
2012
State of the LOD Cloud1
70
• Total Datasets:
295
1 Version 0.3, 09/19/2011
http://www4.wiwiss.fu-berlin.de/lodcloud/state
• Total Triples:
31,634,213,770
Distribution of triples by domain
State of the LOD Cloud1
71
• Total (Out-)Links:
503,998,829
1 Version 0.3, 09/19/2011
http://www4.wiwiss.fu-berlin.de/lodcloud/state
Distribution of links by domain
Exploring theWeb of Data
72
• Linked Data browsers
• Linked Data mashups
• Search engines
Linked Data Browsers
73
http://marbles.sourceforge.net
Marbles
Linked Data Mashup
74
http://revyu.com
Revyu.com
Linked Data Mashup
75
DBPedia Mobile
Pictures from revyu.com
http://wiki.dbpedia.org/DBPediaMobile
Linked Data Mashup
76
http://sig.ma
SIGMA
Linked Data Search Engines
77
http://data.nytimes.com/schools/schools.html
NYTimes
Some Application Scenarios
78
BBC
Some Application Scenarios
79
LinkedGeoData.org
Some Application Scenarios
80
Linked Government Data: USA
Some Application Scenarios
81
Linked Government Data: UK
Summary
82
In this chapter we studied:
• The Web and its evolution.
• Web technology basics: HTTP, HTML, URI.
• Vocabularies to describe data.
• The Semantic Web stack: RDF, RDF-S, OWL, SPARQL.
• Linked Data concept and principles.
• Evolution of the LOD cloud.
• Browsers, mashups and search engines to explore the Web
of Data.
• Some application scenarios.
For exercises, quiz and further material visit our website:
EUCLID - Providing Linked Data 83
@euclid_project euclidproject euclidproject
http://www.euclid-project.eu
Other channels:
eBook Course
Acknowledgements
• Alexander Mikroyannidis
• Alice Carpentier
• Andreas Harth
• Andreas Wagner
• Andriy Nikolov
• Barry Norton
• Daniel M. Herzig
• Elena Simperl
• Günter Ladwig
• Inga Shamkhalov
• Jacek Kopecky
• John Domingue

• Juan Sequeda
• Kalina Bontcheva
• Maria Maleshkova
• Maria-Esther Vidal
• Maribel Acosta
• Michael Meier
• Ning Li
• Paul Mulholland
• Peter Haase
• Richard Power
• Steffen Stadtmüller
84

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Usage of Linked Data: Introduction and Application Scenarios

  • 1. Usage of Linked Data Introduction and Application Scenarios Presented by: Barry Norton
  • 2. Agenda 1. Motivation Scenario 2. Linked Data Foundations 3. Introduction to Linked Data 4. Linked Data use case scenarios 2
  • 4. Music! 4 • Provision of a music-based portal. • Bring together a number of disparate components of data-oriented content: 1. Musical content (streaming data & downloads) 2. Music and artist metadata 3. Review content 4. Visual content (pictures of artists & albums)
  • 5. Music! 5 Visualization Module Metadata Streaming providers Physical Wrapper Downloads Dataacquisition D2R Transf.LD Wrapper Musical Content Application Analysis & Mining Module LDDatasetAccess LD Wrapper RDF/ XML Integrated Dataset Interlinking Cleansing Vocabulary Mapping SPARQL Endpoint Publishing RDFa Other content
  • 6. Music! 6 Expected Results • The developer will contribute back the aggregated and interlinked content to the Linked Open Data Cloud. • Linking of artists will be improved. • Metadata, visual content and reviews will be improved. • Links to emerging Web technologies that inherit from semantics: Google RichSnippets, Facebook OpenGraph and schema.org annotation.
  • 8. • Extension of the technology of computer networks. • The technology supporting the Internet includes the Internet Protocol (IP) . • Each computer on the Internet is assigned an IP number. • Messages can be routed from one computer to another. Internet 8
  • 10. • There is a wealth of information on the Web. • It is aimed mostly towards consumption by humans as end-users: • Recognize the meaning behind content and draw conclusions, • Infer new knowledge using context and • Understand background information. TheWeb 10
  • 11. TheWeb 11 • Billions of diverse documents online, but it is not easily possible to automatically: • Retrieve relevant documents. • Extract information. • Combine information in a meaningful way. • Idea: • Also publish machine processable data on the web. • Formulate questions in terms understandable for a machine. • Do this in a standardized way so machines can interoperate. • The Web becomes a Web of Data • This provides a common framework to share knowledge on the Web across application boundaries.
  • 12. TheWeb: Evolution 12 Web of Documents Web of Data "Documents" Hyperlinks Typed Links "Things"
  • 14. WebTechnology Basics 14 HTML – HyperText Markup Language • Language for displaying web pages and other information in a web browser. • HTML elements consist of tags (enclosed in angle brackets), attributes and content. HTTP – HypertextTransfer Protocol • Foundation of data communication for the WWW. • Client-server protocol. • Every interaction is based on: request and response.
  • 15. WebTechnology Basics 15 Uniform Resource Identifier (URI) • Compact sequence of characters that identifies an abstract or physical resource. • Examples: ldap://[2001:db8::7]/c=GB?objectClass?one mailto:John.Doe@example.com news:comp.infosystems.www.servers.unix tel:+1-816-555-1212 telnet://192.0.2.16:80/ urn:oasis:names:specification:docbook:dtd:xml:4.1.2 http://dbpedia.org/resource/Karlsruhe
  • 16. Describing Data 16 Vocabularies • Collections of defined relationships and classes of resources. • Classes group together similar resources. • Terms from well-known vocabularies should be reused wherever possible. • New terms should be define only if you can not find required terms in existing vocabularies.
  • 17. Describing Data 17 Vocabularies A set of well-known vocabularies has evolved in the Semantic Web community. Some of them are: Vocabulary Description Classes and Relationships Friend-of-a-Friend (FOAF) Vocabulary for describing people. foaf:Person, foaf:Agent, foaf:name, foaf:knows, foaf:member Dublin Core (DC) Defines general metadata attributes. dc:FileFormat, dc:MediaType, dc:creator, dc:description Semantically-Interlinked Online Communities (SIOC) Vocabulary for representing online communities. sioc:Community, sioc:Forum, sioc:Post, sioc:follows, sioc:topic Music Ontology (MO) Provides terms for describing artists, albums and tracks. mo:MusicArtist, mo:MusicGroup, mo:Signal, mo:member, mo:record Simple Knowledge Organization System (SKOS) Vocabulary for representing taxonomies and loosely structured knowledge. skos:Concept, skos:inScheme, skos:definition, skos:example
  • 18. Describing Data 18 Vocabularies More extensive lists of well-known vocabularies are maintained by: • W3C SWEO Linking Open Data community project http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/CommonVocabularies • Mondeca: Linked Open Vocabularies http://labs.mondeca.com/dataset/lov • Library Linked Data Incubator Group: Vocabularies in the library domain http://www.w3.org/2005/Incubator/lld/XGR-lld-vocabdataset-20111025
  • 19. Semantics on theWeb 19 Semantic Web Stack Berners-Lee (2006)
  • 20. Semantics on theWeb 20 Semantic Web Stack Berners-Lee (2006) Syntatic basis Basic data model Simple vocabulary (schema) language Expressive vocabulary (ontology) language Query language Application specific declarative-knowledge Digital signatures, recommendations Proof generation, exchange, validation
  • 21. Semantics on theWeb 21 Semantic Web Stack Berners-Lee (2006) RDF – Resource Description Framework
  • 22. Semantics on theWeb 22 RDF – Resource Description Framework • RDF is the basis layer of the Semantic Web stack ‘layer cake’. • Basic building block: RDF triple. • Subject – a resource, which may be identified with a URI. • Predicate – a URI-identified reused specification of the relationship. • Object – a resource or literal to which the subject is related.
  • 23. Semantics on theWeb 23 RDF – Resource Description Framework (Example) <http://musicbrainz.org/artist/b10bbbfc- cf9e-42e0-be17-e2c3e1d2600d#_> <http://www.w3.org/2002/07/owl#sameAs> <http://dbpedia.org/resource/The_Beatles>. <http://musicbrainz.org/artist/b10bbbfc- cf9e-42e0-be17-e2c3e1d2600d#_> <http://xmlns.com/foaf/0.1/name> "The Beatles" . URIs are given in angle brackets in N-Triples. Literals are given in quotes in N-Triples. In N-Triples every statement is terminated with a full stop.
  • 24. Semantics on theWeb 24 RDF Graphs • Every set of RDF assertions can then be drawn and manipulated as a (labelled directed) graph: • Resources – the subjects and objects are nodes of the graph. • Predicates – each predicate use becomes a label for an arc, connecting the subject to the object. Subject Object Predicate
  • 26. Semantics on theWeb 26 RDF Blank Nodes • RDF graphs can also contain unidentified resources, called blank nodes: • Blank nodes can group related information, but their use in Linked Data is discouraged. [] <http://www.w3.org/2003/01 /geo/wgs84_pos#Point> <http://www.w3.org/1999/02/ 22-rdf-syntax-ns#type> <http://www.w3.org/2003/01 /geo/wgs84_pos#lat> <http://www.w3.org/2003/01 /geo/wgs84_pos#long> 49.005 8.386
  • 27. Semantics on theWeb 27 RDFTurtle • Turtle is a syntax for RDF more readable. • Since many URIs share same basis we use prefixes: @prefix rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>. @prefix rdfs:<http://www.w3.org/2000/01/rdf-schema#>. @prefix owl:<http://www.w3.org/2002/07/owl#>. @prefix mo:<http://purl.org/ontology/mo/>. @prefix dbpedia:<http://dbpedia.org/resouce/>. And (sometimes) a unique base: @base <http://musicbrainz.org/>.
  • 28. Semantics on theWeb 28 RDFTurtle • Also has a simple shorthand for class membership: @base <http://musicbrainz.org/>. @prefix mo:<http://purl.org/ontology/mo/>. <artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> a mo:MusicGroup. Is equivalent to: <http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/ontology/mo/MusicGroup>.
  • 29. RDFTurtle • When multiple statements apply to same subject they can be abbreviated as follows: <artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> rdfs:label "The Beatles"; owl:sameAs dbpedia:The_Beatles , <http://www.bbc.co.uk/music/artists/ b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#artist> . Same subject & predicate Semantics on theWeb 29 Same subject
  • 30. RDFTurtle • Turtle also provides a simple syntax for datatypes and language tags for literals, respectively: <recording/5098d0a8-d3c3-424e-9367-1f2610724410#_> a mo:Signal; rdfs:label "All You Need Is Love" ; mo:duration "PT3M48S"^^xsd:duration . dbpedia:The_Beatles dbpedia-owl:abstract "The Beatles were an English rock band formed (…) "@en, "The Beatles waren eine britische Rockband in den (…) "@de . Semantics on theWeb 30
  • 31. Semantics on theWeb 31 RDF/XML • This is most useful for inter-machine communication. • The primary (recurring) element in encoding assertions (thereby triples) is rdf:Description, e.g.: <rdf:Description rdf:about="http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_"> <foaf:name>The Beatles</foaf:name> <owl:sameAs rdf:resource="http://dbpedia.org/resource/The_Beatles"> </rdf:Description> <rdf:Description rdf:about="http://musicbrainz.org/artist/4d5447d7-c61c-4120-ba1b-d7f471d385b9#_"> <foaf:name>John Lennon</foaf:name> </rdf:Description>
  • 32. Semantics on theWeb 32 Semantic Web Stack Berners-Lee (2006) RDF-S – RDF Schema
  • 33. Semantics on theWeb 33 RDF-S – RDF Schema Language for two tasks w.r.t. the RDF data model: • Expectation – nominate: • the ‘types’, i.e., classes, of things we might make assertions about, and • the properties we might apply, as predicates in these assertions, to capture their relationships. • Inference – given a set of assertions, using these classes and properties, specify what should be inferred about assertions that are implicitly made.
  • 34. Semantics on theWeb 34 RDF-S – RDF Schema • rdf:Property - Class of RDF properties. Example: mo:member - Indicates a member of a musical group. • rdfs:domain - States that any resource that has a given property is an instance of one or more classes. mo:member rdfs:domain mo:MusicGroup . • rdfs:range - States that the values of a property are instances of one or more classes. mo:member rdfs:range foaf:Agent .
  • 35. Semantics on theWeb 35 RDF-S – RDF Schema mo:MusicGroup rdfs:subClassOf foaf:Group . <artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> rdf:type mo:MusicGroup . <artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> rdf:type foaf:Group . Schema Existing fact Inferred fact We expect to use this vocabulary to make assertions about music groups. Having made such an assertion... Inferences can be drawn that we did not explicitly make
  • 36. Semantics on theWeb 36 RDF-S – RDF Schema Resources and predicates with (limited) inferences: rdfs:Resource rdfs:Literal, rdfs:Datatype rdfs:Class, rdfs:subClassOf rdfs:subPropertyOf rdfs:range, rdfs:domain Some predicates with NO inferences: rdfs:comment rdfs:label rdfs:seeAlso rdfs:isDefinedBy rdf:Property (an instance of rdfs:Class)
  • 37. Semantics on theWeb 37 Semantic Web Stack Berners-Lee (2006) OWL – Web Ontology Language
  • 38. Semantics on theWeb 38 OWL – Web Ontology Language • RDFS provides a simplified ontological language for defining vocabularies about specific domains. • Sometimes it is necessary to have access to a wider range of ontological constructs. • Web Ontology Language (OWL) provides more ontological constructs and avoids some of the potential confusion in RDF-S.
  • 39. Semantics on theWeb 39 OWL 2.0 – Web Ontology Language 2.0 Extends the DL further, but has three more computable fragments (profiles). OWL 2 Full • Used informally to refer to RDF graphs considered as OWL 2 ontologies and interpreted using the RDF-Based Semantics. OWL 2 DL • Used informally to refer to OWL 2 DL ontologies interpreted using the Direct Semantics. OWL 2 EL • Limited to basic classification, but with polynomial-time reasoning. OWL 2 QL • Designed to be translatable to relational database querying. OWL 2 RL • Designed to be efficiently implementable in rule- based systems. OWL 2 Full OWL 2 DL OWL 2 EL OWL 2 QL OWL 2 RL
  • 40. Semantics on theWeb 40 OWL – Web Ontology Language OWL is made up of terms which provide for: • Class construction: forming new classes from membership of existing ones (e.g., unionOf, intersectionOf, etc.). • Property construction: distinction between OWL ObjectProperties (resources as values) and OWL DatatypeProperties (literals as values). • Class axioms: sub-class, equivalence and disjointness relationships. • Property axioms: sub-property relationship, equivalence and disjointness, and relationships between properties. • Individual axioms: statements about individuals (sameIndividual, differentIndividuals).
  • 41. Semantics on theWeb 41 Semantic Web Stack Berners-Lee (2006) SPARQL – * Protocol and RDF Query Language
  • 42. Semantics on theWeb 42 SPARQL – * Protocol and RDF Query Language • Query language designed to use a syntax similar to SQL for retrieving data from relational databases. • Different query forms: • SELECT returns variables and their bindings directly. • CONSTRUCT returns a single RDF graph specified by a graph template. • ASK test whether or not a query pattern has a solution. Returns yes/no. • DESCRIBE returns a single RDF graph containing RDF data about resources.
  • 43. Semantics on theWeb 43 SPARQL – * Protocol and RDF Query Language • The syntax of a SELECT query is as follows: – SELECT nominates which components of the matches made against the data should be returned. – FROM (optional) indicates the sources for the data against which to find matches. – WHERE defines patterns to match against the data. – ORDER BY defines a means to order the selected matches.
  • 44. Semantics on theWeb 44 SPARQL – * Protocol and RDF Query Language PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX music-ont: <http://purl.org/ontology/mo/> SELECT ?album_name ?track_title WHERE { <http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> foaf:made ?album . ?album dc:title ?album_name ; music-ont:track ?track . ?track dc:title ?track_title . } Retrieve the names of the albums and tracks recorded byThe Beatles.
  • 45. Semantics on theWeb 45 SPARQL – * Protocol and RDF Query Language SQL SPARQL Based on relations (tables). Based on labelled directed graphs. The relations (tables) to be matched over should be indicated. Assumes a default graph. (The FROM clause populates this with specific identified subgraphs). (Retrieval) queries produce a relation from a relation. SPARQL SELECT queries produce a relation from a graph. CONSTRUCT queries (considered later) produce a graph from a graph.
  • 46. Semantics on theWeb 46 SPARQL – * Protocol and RDF Query Language • SPARQL 1.1 provides graph update operations: • INSERT DATA: adds explicit triples, given inline. • DELETE DATA: removes explicit triples, given inline. • DELETE/INSERT WHERE: updates based on triples calculated from WHERE clause (as in SELECT and CONSTRUCT). • LOAD: reads the content of a document into a graph. • COPY/MOVE/APPEND: manipulates at named graph level. • CLEAR/DROP: removes all triples in one or more graph.
  • 47. Semantics on theWeb 47 SPARQL – * Protocol and RDF Query Language PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX foaf: <http://xmlns.com/foaf/0.1/> INSERT DATA { GRAPH <http://myFavGroups/The_Beatles> { <http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d#_> foaf:made <http://musicbrainz.org/release/3a685770-7326-34fc-9f18-e5f5626f3dc5#_> , < http://musicbrainz.org/release/cb6f8798-d51e-4fa5-a4d1-2c0602bfe1b6#_> . <http://musicbrainz.org/release/3a685770-7326-34fc-9f18-e5f5626f3dc5#_> dc:title "Please Please Me". < http://musicbrainz.org/release/cb6f8798-d51e-4fa5-a4d1-2c0602bfe1b6#_ > dc:title "Something New". } } Insert the following albums recorded byThe Beatles into the graph http://myFavGroups/The_Beatles
  • 48. Semantics on theWeb 48 SPARQL – * Protocol and RDF Query Language PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX foaf: <http://xmlns.com/foaf/0.1/> DELETE { ?album ?predicate ?object . } WHERE { <http://musicbrainz.org/artist/b10bbbfc-cf9e-42e0-be17-e2c3e1d2600d> foaf:made ?album . ?album dc:title "Casualities"; ?predicate ?object .} Delete all the information about the album Casualities of The Beatles.
  • 50. Linked Data 50 • Set of best practices for publishing data on the Web. • Data from different knowledge domains, self-described, linked and accessible. • Follows 4 simple principles…
  • 51. Linked Data Principles 1. Use URIs as names for things. 2. Use HTTP URIs so that users can look up those names. 3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL). 4. Include links to other URIs, so that users can discover more things. 51
  • 52. Linked Data Principles • A foundational issue in Linked Data was the distinction of URIs for object documents that might describe them. 52 1. Use URIs as names for things.
  • 53. Linked Data Principles • HTTP allows a second way to distinguish real-world objects from documents. • Best practice says HTTP 303 and Location header should be used. 53 2. Use HTTP URIs so user can look up those names.
  • 54. • While RDF/XML should be the default for look-up. • RDFa annotations in HTML are now also standard. • SPARQL endpoint for queries are encouraged, or a dump of the whole dataset. Linked Data Principles 54 3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL, Turtle1). 1 To become a standard.
  • 55. What to return for a URI? • Immediate description: triples where the URI is the subject. • Backlinks: triples where the URI is the object. • Related descriptions: information of interest in typical usage scenarios. • Metadata: information as author and licensing information. • Syntax: RDF descriptions as RDF/XML and human-readable formats. Linked Data Principles 55 3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL, Turtle1). Source: How to Publish Linked Data on The Web - Chris Bizer, Richard Cyganiak, Tom Heath.
  • 56. There are several ways to reuse URIs: • direct reuse • (OWL) sameAs • (RDFS) seeAlso • direct reuse of class/property • (RDFS) sub-class/-property • (OWL) equivalent class/property • SKOS broad match Linked Data Principles 56 4. Include links to other URIs, so that users can discover more things. Instance Level Schema Level
  • 57. Linked Data 5 Star 57 Data is available on the Web. Data is available as machine-readable structured data. Non-propietary formats are used. Individual data identified with open standards. Data is linked to other data provider.
  • 58. Linked Data 5 Star 58 Example: "John Lennon" "Paul McCartney" "George Harrison" "Ringo Starr" My Data
  • 59. Linked Data 5 Star 59 Data is available on the Web "John Lennon" "Paul McCartney" "George Harrison" "Ringo Starr" My Data It can be retrieved using HTTP.
  • 60. Linked Data 5 Star 60 Data is available as machine-readable structured data "John Lennon" "Paul McCartney" "George Harrison" "Ringo Starr" My Data "The Beatles" http://upload.wikimedia.org/wi kipedia/commons/thumb/d/df/ The_Fabs.JPG/600px- The_Fabs.JPG Please Please Me – 1963 A Hard Day‘s Night – 1964 Help! – 1965 Revolver – 1966 … Images Scanned Information Plain text or … (to continue on the next slide) Machine-readable data:
  • 61. Non-propietary formats are used <schema "http://www.example.com/2012/XMLMyMusic" version= "1.0" > <band> <name>The Beatles</name> <member>John Lennon</member> <member>Paul McCartney</member> <member>George Harrison</member> <member>Ringo Starr</member> <picture>http://upload.wikimedia.org/wikipedia/common s/thumb/d/df/The_Fabs.JPG/600px- The_Fabs.JPG</picture> <album year=1963>Please Please Me</album> <album year=1964>A Hard Day‘s Night</album> <album year=1965>Help!</album> <album year=1966>Revolver</album> … </band> Linked Data 5 Star 61 My Data
  • 62. Linked Data 5 Star 62 Individual data identified with open standards <schema "http://www.example.com/2012/XMLMyMusic" version= "1.0" > <band> <name>http://musicbrainz.org/artist/b10bbbfc-cf9e- 42e0-be17-e2c3e1d2600d</name> <member>http://musicbrainz.org/artist/4d5447d7- c61c-4120-ba1b-d7f471d385b9</member> ... <album year=1963>http://musicbrainz.org/release/5f3ba07b- 4a24-4cd5-b8ad-95ba0fcebec1</album> … </band> My Data In this context, "Revolver" is an album! Not a gun. URI: Uniform Resource Identifier • Data is uniquely identified The Beatles John Lennon Revolver • Dissambiguation
  • 63. Linked Data 5 Star 63 Data is linked to other data provider <schema "http://www.example.com/2012/XMLMyMusic" version= "1.0" > <band> <name>http://musicbrainz.org/artist/b10bbbfc-cf9e- 42e0-be17-e2c3e1d2600d</name> <member>http://musicbrainz.org/artist/4d5447d7- c61c-4120-ba1b-d7f471d385b9</member> ... <album year=1963>http://musicbrainz.org/release/5f3ba07b- 4a24-4cd5-b8ad-95ba0fcebec1</album> … <seeAlso>http://dbpedia.org/resource/The_Beatles </seeAlso> </band> http://dbpedia.org/resource/ The_Beatles
  • 70. State of the LOD Cloud1 70 • Total Datasets: 295 1 Version 0.3, 09/19/2011 http://www4.wiwiss.fu-berlin.de/lodcloud/state • Total Triples: 31,634,213,770 Distribution of triples by domain
  • 71. State of the LOD Cloud1 71 • Total (Out-)Links: 503,998,829 1 Version 0.3, 09/19/2011 http://www4.wiwiss.fu-berlin.de/lodcloud/state Distribution of links by domain
  • 72. Exploring theWeb of Data 72 • Linked Data browsers • Linked Data mashups • Search engines
  • 75. Linked Data Mashup 75 DBPedia Mobile Pictures from revyu.com http://wiki.dbpedia.org/DBPediaMobile
  • 77. Linked Data Search Engines 77 http://data.nytimes.com/schools/schools.html NYTimes
  • 80. Some Application Scenarios 80 Linked Government Data: USA
  • 82. Summary 82 In this chapter we studied: • The Web and its evolution. • Web technology basics: HTTP, HTML, URI. • Vocabularies to describe data. • The Semantic Web stack: RDF, RDF-S, OWL, SPARQL. • Linked Data concept and principles. • Evolution of the LOD cloud. • Browsers, mashups and search engines to explore the Web of Data. • Some application scenarios.
  • 83. For exercises, quiz and further material visit our website: EUCLID - Providing Linked Data 83 @euclid_project euclidproject euclidproject http://www.euclid-project.eu Other channels: eBook Course
  • 84. Acknowledgements • Alexander Mikroyannidis • Alice Carpentier • Andreas Harth • Andreas Wagner • Andriy Nikolov • Barry Norton • Daniel M. Herzig • Elena Simperl • Günter Ladwig • Inga Shamkhalov • Jacek Kopecky • John Domingue
 • Juan Sequeda • Kalina Bontcheva • Maria Maleshkova • Maria-Esther Vidal • Maribel Acosta • Michael Meier • Ning Li • Paul Mulholland • Peter Haase • Richard Power • Steffen Stadtmüller 84