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DATA
SUPPORT
OPEN
Linked Open Data
Principles,
Technologies and
Examples
PwC firms help organisations and individuals create the value they’re looking for. We’re a network of firms in 158 countries with close to 180,000 people who are committed to
delivering quality in assurance, tax and advisory services. Tell us what matters to you and find out more by visiting us at www.pwc.com.
PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details.
DATASUPPORTOPEN
Learning objectives
By the end of the course, participants should have a clear
understanding of:
• What linked open data is;
• What is the difference between linked and open data;
• How to publish linked data;
• The economic and social aspects of linked data;
• How linked data technologies can be applied to improve the
availability, understandability and usability of EU data.
Slide 2
DATASUPPORTOPEN
Content
This training consists of 3 modules:
1. Introduction to linked data
2. Introduction to RDF & SPARQL
3. Workshop on publishing open linked EU data
Slide 3
DATASUPPORTOPEN
Learning Module 1:
Introduction to Linked Data
Slide 4
DATASUPPORTOPEN
Introduction to linked data
This module contains ...
• An introduction to the linked data principles;
• The expected benefits of linked data;
• An introduction to linked data technologies;
• An outline of the 5-star scheme for publishing linked data;
• An overview of linked data initiatives in Europe.
Slide 5
Find more on: training.opendatasupport.eu
DATASUPPORTOPEN
What is linked data?
Evolution from a document-based Web to a Web of
interlinked data.
Slide 6
DATASUPPORTOPEN
The Web is evolving from a “Web of linked
documents” into a “Web of linked data”
• The Web started as a collection of
documents published online – accessible at a
Web location identified by a URL.
• These documents often contain data about
real-world resources which is mainly
human-readable and cannot be understood
by machines.
• The Web of Data is about enabling the access
to this data, by making it available in
machine-readable formats and connecting it
using Uniform Resource Identifiers (URIs),
thus enabling people and machines to collect
the data, and put it together to do all kinds
of things with it (permitted by the licence).
Machine-readable data
(or metadata) is data in a
format that can be interpreted
by a computer.
2 types of machine-readable
data exist:
• human-readable data that
is marked up so that it can
also be understood by
computers, e.g.
microformats, RDFa;
• data formats intended
principally for computers,
e.g. RDF, XML and JSON.
Slide 7
See also:
http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
http://linkeddatabook.com/editions/1.0/
DATASUPPORTOPEN
Defining linked data
Providing data as a service
“Linked data is a set of design principles for sharing machine-readable
data on the Web for use by public administrations, business and
citizens.”
EC ISA Case Study: How Linked Data is transforming eGovernment
The four design principles of Linked Data (by Tim Berners Lee):
1. Use Uniform Resource Identifiers (URIs) as names for things.
2. Use HTTP URIs so that people 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 they can discover more things.
Slide 8
See also:
http://www.youtube.com/watch?v=4x_xzT5eF5Q
http://www.w3.org/DesignIssues/LinkedData.html
http://www.youtube.com/watch?v=uju4wT9uBIA
DATASUPPORTOPEN
The value proposition of linked (open)
government data
• Flexible data integration: facilitates data integration and enables
the interconnection of previously disparate government datasets.
• Efficiency gains in data integration– the network effect: the
addition of each new dataset increases the value of those datasets
that are already published!
• Ease of navigation: makes browsing through complex data easier
via URIs.
• Increase in data quality:
 The use of URIs leads to improved data management and quality.
 The increased (re)use triggers a growing demand to improve data quality.
Through crowd-sourcing and self-service mechanisms, errors are
progressively corrected.
9
See also:
ISA Study on Business Models for LOGD
https://joinup.ec.europa.eu/community/semic/document/study-
business-models-linked-open-government-data-bm4logd
DATASUPPORTOPEN
The value proposition of linked (open)
government data
• Increase in data usability by providing data as a service:
 Resolvable URIs
 Data is available in different formats, not limited to RDF, e.g. XML, CSV,
text, JSON…
• Compatible with existing standards and technologies: a
linked data infrastructure can provide access to homogenised,
linked, and enriched data using standard Web-based interfaces
(such as HTTP and SPARQL) and Web-based languages (such as
XHTML, RDF+XML), on top of either:
 Existing relational/spatial database systems, by applying database-to-RDF
conversions; or
 Existing XML/file-based data.
10
DATASUPPORTOPEN
The value proposition of linked (open)
government data
• Ease of model updates: RDF data models and vocabularies can be
extended, adapted and updated more easily. Changes can be reflected
on the data with lower costs and effort (compared to traditional
relational databases).
• Cost reduction: The reuse of LOGD in e-Government applications
leads to considerable cost reductions, when it comes to service
integration, data use, reuse, and exchange.
• New services: The availability of LOGD gives rise to new,
integrated, services offered by the public and/or private sector.
11
See also:
ISA Study on Business Models for LOGD
https://joinup.ec.europa.eu/community/semic/document/study-
business-models-linked-open-government-data-bm4logd
DATASUPPORTOPEN
The four principles of linked data in practice
1. Use Uniform Resource Identifiers (URIs) as names for things.
2. Use HTTP URIs so that people can look up those names.
E.g. for an organisation: UNICEF in EuroVoc.
- http://eurovoc.europa.eu/1022
Slide 12
DATASUPPORTOPEN
The four principles in practice
3. When someone looks up a URI, provide useful information, using the
standards (RDF, SPARQL).
4. Include links to other URIs so that people/machines can discover more
things.
Slide 13
DATASUPPORTOPEN
Linked data vs. open data
Open data
Data can be published and be
publicly available under an open
licence without linking to other
data sources.
Linked data
Data can be linked to URIs from
other data sources, using open
standards such as RDF without
being publicly available under an
open licence.
Slide 14
“Open data is data that can be freely used, reused and redistributed by
anyone – subject only, at most, to the requirement to attribute and share-
alike.”
- OpenDefinition.org
See also:
Cobden et al., A research agenda for Linked Closed Data
http://ceur-ws.org/Vol-782/CobdenEtAl_COLD2011.pdf
DATASUPPORTOPEN
Linked data foundations
URIs for naming things, RDF for describing data and
SPARQL for querying linked data.
Slide 15
DATASUPPORTOPEN
Uniform Resource Identifier (URI)
“A Uniform Resource Identifier (URI) is a compact sequence of characters that
identifies an abstract or physical resource.”
– ISA’s 10 Rules for Persistent URIs
A country, e.g. Belgium
- http://publications.europa.eu/resource/authority/country/BEL
An organisation, e.g. the Publications Office
- http://publications.europa.eu/resource/authority/corporate-body/PUBL
A dataset, e.g. Countries Named Authority List
- http://publications.europa.eu/resource/authority/country/
Slide 16
BE
See also:
http://www.slideshare.net/OpenDataSupport/design
-and-manage-persitent-uris
DATASUPPORTOPEN
RDF & SPARQL
The Resource Description Framework (RDF ) is a syntax for representing
data and resources on the Web
Slide 17
RDF breaks every piece of information down in triples:
• Subject – a resource, which is identified with a URI.
• Predicate – a URI-identified reused specification of the relationship.
• Object – a resource or literal to which the subject is related.
SPARQL is a standardised language for querying RDF data.
http://example.org/place/Brussels is the capital of “Belgium”.
OR
http://example.org/place/Brussels is the capital of http://example.org/place/Belgium.
Subject Predicate Object
See also:
http://www.slideshare.net/OpenDataSupport/introduction-to-rdf-sparql
DATASUPPORTOPEN
How to publish linked data?
Paving the way towards 5-star linked data
Slide 18
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5 star-schema of Linked Open Data
★
Make your stuff available on the Web (whatever format)
under an open license.
★★
Make it available as structured data (e.g., Excel instead of
image scan of a table).
★★★ Use non-proprietary formats (e.g., CSV instead of Excel).
★★★★
Use URIs to denote things, so that people can point at your
stuff.
★★★★★ Link your data to other data to provide context.
Slide 19
DATASUPPORTOPEN
★ Make your stuff available on the Web under an
open licence
Slide 20
Trends, risks and
vulnerabilities in
securities markets
DATASUPPORTOPEN
★ ★ Make it available as structured data
Slide 21
Waterbase - Emissions to water:
CountryCode
DATASUPPORTOPEN
★ ★ ★ Use non-proprietary formats
• Proprietary: Excel, Word, PDF...
• Non-proprietary: XML, CSV, RDF, JSON, ODF...
DG Enlargement - Regional programmes:
Slide 22
DATASUPPORTOPEN
★ ★ ★ ★ Use URIs to denote things
Slide 23
See also:
http://www.slideshare.net/OpenDataSupport/design-and-manage-persitent-uris
Food Additives - http://open-data.europa.eu/en/data/dataset/1gXgb0Yj73R4ttDChQ5Wyg
DATASUPPORTOPEN
★ ★ ★ ★ ★ Link your data to other data to provide
context
Slide 24
Corporate bodies Named Authority Lists - http://open-data.europa.eu/en/data/dataset/corporate-body
DATASUPPORTOPEN
LOGD roadblocks
• Necessary investments.
• Lack of necessary competencies.
• Perceived lack of tools.
• Lack of service level guarantees.
• Missing, restrictive, or incompatible licences.
• Surfeit of standard vocabularies.
• The inertia of the status quo – change is accomplished slowly.
25
See also:
ISA Study on Business Models for LOGD
https://joinup.ec.europa.eu/community/semic/document/study-business-
models-linked-open-government-data-bm4logd
DATASUPPORTOPEN
Linked data initiatives in
Europe
Examples on supra-national, national, regional and
private initiatives in the area of linked data.
Slide 26
DATASUPPORTOPEN
EU institutions initiatives – some examples
• European Environment Agency SPARQL endpoint:
Tool allowing searching for linked data published by the the European Environment
Agency. http://semantic.eea.europa.eu/sparql
• EU Open Data Portal SPARQL endpoint:
Allows searching for linked metadata of datasets published on the EU Open Data Portal.
https://open-data.europa.eu/en/linked-data
• Publications Office of the EU - CELLAR SPARQL Endpoint:
Allows searching for linked data published by the Publications Office of the EU, such as
legislation data, publications data, etc. http://publications.europa.eu/en/linked-data
• DG SANTE SPARQL endpoint:
Tool for querying linked open data on European Community Health Indicators, the EU
Register of Health Claims, etc. http://ec.europa.eu/semantic_webgate/
• Europeana SPARQL endpoint:
Tool allowing querying a multi-lingual online collection of millions of digitized items
from European museums, libraries, archives and multi-media collections.
http://labs.europeana.eu/api/linked-open-data-SPARQL-endpoint
Slide 27
DATASUPPORTOPEN
Initiatives funded by the European Commission
Slide 28
ADMS.
SW
CORE
VOCABULARY
PUBLIC
SERVICE
DATASUPPORTOPEN
Member State initiatives – some examples
DE – Bibliotheksverbund Bayern
Linked data from 180 academic libraries in Bavaria, Berlin and Brandenburg.
IT – Agenzia per l’Italia digitiale
Three datasets published as linked data: the Index of Public Administration, the SPC
contracts for web services and conduction systems and the Classifications for the data in
Public Administration.
NL – Building and address register
The Dutch Address and Buildings base register published as linked data.
UK – Ordnance Survey
Three OS Open Data products published as linked data: the 1:50 000 Scale Gazetteer,
Code-Point Open and the administrative geography taken from Boundary Line.
UK – Companies House
Publishing basic company details as linked data using a simple URI for each company in
their database.
Slide 29
See also:
ISA Study on Business Models for LOGD
https://joinup.ec.europa.eu/community/semic/document/study-business-
models-linked-open-government-data-bm4logd
DATASUPPORTOPEN
Member States Initiatives – UK National Archives
Slide 30
Three considerations for legislation as data
• Typographic layout
• Versioning / changes over time
• Semantics
Semantic representation using RDF and Linked Data
• URIs for things & RDF data model
Requires granular URIs to name things
• Identifier: http://www.legislation.gov.uk/id/{type}/{year}/{number}/section/{number}
• Representation: .../data.[xml | xht | pdf | rdf | feed]
Source: https://www.nationalarchives.gov.uk/documents/information-management/open-and-linked-data-johnsheridan.ppt
See also:
The European Legal Identifier (ELI)
http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=URISERV:jl0068
DATASUPPORTOPEN
Member States Initiatives – UK National Archives
Slide 31
Versioning of legislation in RDF
http://www.legislation.gov.uk/id/ukpga/2010/32/section/12/4/data.rdf
DATASUPPORTOPEN
Open & linked data at BBC
• BBC Things, the open data website of BBC, allows anyone to access the data
that BBC stores about data on the places, people and organisations that appear
in BBC programmes and online content.
• This data already powers large parts of the BBC website, including BBC News and
Sport.
• BBC Things is part of the BBC Linked Data Platform, which provides public
access to data stored in the BBC platform and provides a public reference for all of
the things that the BBC creates content about.
Slide 32
Further reading:
http://www.bbc.co.uk/things/search?q=juncker
http://www.forbes.com/sites/bernardmarr/2015/06/01/how-big-data-drives-success-at-rolls-royce/
DATASUPPORTOPEN Slide 33
Open & linked data at BBC
DATASUPPORTOPEN
Data Value Chains using Linked Data at
Volkswagen
Slide 34
Source: Sören Auer, SEMIC Conference 2015, Riga
https://joinup.ec.europa.eu/sites/default/files/isa_field_path/presentation_by_soren_auer_-_creating_data_value_chains_by_linking_enterprise_data.pdf
DATASUPPORTOPEN
1. Link databases
“I’d like to know last month’s production volume total for all fields in which GDF
Suez E&P Norge is a licensee.”
• Data spread across multiple databases:
GDF Suez data + Norwegian Government data + Wikipedia data
• Need to uniquely identify resources.
2. Add meaning
“I’d like to know all fields operated by Statoil Petroleum AS with their production
volumes.”
• Need for adding semantics in order to allow machine reasoning.
For example:
• Kristin is a field
• Åsgard is an oil platform
• Statoil Petroleum AS is a company
Linked Data in the oil and gas industry
Slide 35
Further reading:
http://www.topquadrant.com/
resources/solutions/docs/Se
mantic-data-oil-and-gas.pdf
DATASUPPORTOPEN
Conclusions
• Linked data is a set of design principles for sharing machine-readable
data on the Web.
• URIs, RDF and SPARQL form the foundational layer for Linked data.
• Linked data offers a number of advantages such as:
o Data integration with small impact on legacy systems;
o Enables for semantic interoperability;
o Easier browsing through complex data;
o Increased data quality;
Slide 36
DATASUPPORTOPEN
Conclusions cont’d
• Linked data offers a number of advantages such as:
o Enables easy updates, adaptations and extensions of data models;
o Cost reduction from the reuse of LOGD in e-Government applications;
o Enables creativity and innovation through context and knowledge-
creation.
Slide 37
DATASUPPORTOPEN
Learning Module 2:
Introduction to RDF & SPARQL
Slide 38
DATASUPPORTOPEN
Introduction to RDF and SPARQL
This module contains ...
• An introduction to the Resource Description Framework (RDF) for
describing your data.
• An introduction to SPARQL on how you can query and manipulate
data in RDF.
Slide 39
Find more on: training.opendatasupport.eu
DATASUPPORTOPEN
Learning objectives
By the end of this training module you should have a clear understanding
of:
• The Resource Description Framework (RDF);
• How to write/read RDF;
• How you can describe your data with RDF;
• What SPARQL is;
• How to understand and write a SPARQL SELECT query.
Slide 40
DATASUPPORTOPEN
Resource Description Framework
An introduction to RDF.
Slide 41
DATASUPPORTOPEN
RDF in the stack of Semantic Web technologies
Resource: Everything that can have
a unique identifier (URI), e.g. pages,
places, people, organisations,
products...
Description: attributes, features,
and relations of the resources
Framework: model, languages and
syntaxes for these descriptions
• Published as a W3C
recommendation in 1999.
• RDF was originally introduced as a
data model for metadata.
• RDF was generalised to cover
knowledge of all kinds.
Slide 42
DATASUPPORTOPEN
Example: RDF description of an organisation
Publications Office, 2, rue Mercier, 2985 Luxembourg, LUXEMBOURG
Slide 43
<rdf:RDF
xmlns:rdfs=“http://www.w3.org/2000/01/rdf-schema#”
xmlns:org=“http://www.w3.org/ns/org#”
xmlns:locn=“http://www.w3.org/ns/locn#” >
<org:Organization rdf:about=“http://publications.europa.eu/resource/authority/corporate-
body/PUBL”>
<rdfs:label> “Publications Office”< /rdfs:label>
<org:hasSite rdf:resource=“http://example.com/site/1234”/>
</org:Organization>
<locn:Address rdf:about=“http://example.com/site/1234”/>
<locn:fullAddress>”2, rue Mercier, 2985 Luxembourg, LUXEMBOURG”</locn:fullAddress>
</locn:Address>
</rdf:RDF>
DATASUPPORTOPEN
RDF structure
Triples, graphs and syntaxes.
Slide 44
DATASUPPORTOPEN
What is a triple?
Slide 45
Every piece of information expressed in RDF is represented as a triple:
• Subject – a resource, which is identified with a URI.
• Predicate – a URI-identified reused specification of the relationship.
• Object – a resource or literal to which the subject is related.
http://publications.europa.eu/resource/authority/file-type/ has a title “File types Name Authority List”.
Subject Predicate Object
Example: name of a dataset:
DATASUPPORTOPEN
RDF Syntax
RDF/XML
Slide 46
<rdf:RDF
xmlns:dcat=“http://www.w3.org/TR/vocab-dcat/“
xmlns:dct=“http://purl.org/dc/terms/”
<dcat:Dataset rdf:about=“http://publications.europa.eu/resource/authority/file-type/”>
<dct:title> “File types Named Authority List”< /dct:title>
<dct:publisher rdf:resource=“http://open-data.europa.eu/en/data/publisher/publ”/>
</dcat:Dataset>
<dct:Agent rdf:about=“http://open-data.europa.eu/en/data/publisher/publ”/>
<dct:title>”Publications Office”</dct:title>
</dct:Publisher>
</rdf:RDF>
Subject
Predicate
Object
Graph
Definition of prefixes
Description of data – triples
DATASUPPORTOPEN
Visual representation (RDF graph) of the triples from
the RDF/XML syntax example
Slide 47
Subject
Predicate
Object
DATASUPPORTOPEN
RDF Syntax
Turtle
Subject
Predicate
Object
Slide 48
@prefix dcat: <http://www.w3.org/TR/vocab-dcat/> .
@prefix dct: <http://purl.org/dc/terms/.
< http://publications.europa.eu/resource/authority/file-type/>
a <dcat:Dataset> ;
dct:title “File types Name Authority List“;
dct:publisher <http://open-data.europa.eu/en/data/publisher/publ> .
<http://open-data.europa.eu/en/data/publisher/publ>
a <dct:Agent> ;
dct:title “Publications Office” .
Graph
See also:
http://www.w3.org/2009/12/rdf-ws/papers/ws11
Definition of prefixes
Description of data – triples
DATASUPPORTOPEN
RDF Syntax
RDFa
Subject
Predicate
Object
Slide 49
<html>
<head> ... </head>
<body>
...
<div resource=“http://publications.europa.eu/resource/authority/file-type/”
typeof= “http://www.w3.org/ns/dcat#Dataset”>
<p>
<span property=" http://purl.org/dc/terms/title ">File types Name Authority List<span>
Publisher: <span property="http://purl.org/dc/terms/Agent"> Publications Office</span>
</p></div>
</body>
See also:
http://www.w3.org/TR/2012/NOTE-rdfa-primer-20120607/
embedding RDF data in HTML
DATASUPPORTOPEN
How to represent data in RDF
Classes, properties and vocabularies
Slide 50
DATASUPPORTOPEN
RDF Vocabulary
“A vocabulary is a data model comprising classes, properties and
relationships which can be used for describing your data and
metadata.”
• Class. A construct that represents things in the real and/or information world, e.g. a
person, an organisation, a concept such as “health” or “freedom”.
• Property. A characteristic of a class in a particular dimension such as the legal name
of an organisation or the date and time that an observation was made. In RDF
properties are encoded as data type properties.
• Relationship. A link between two classes; for example the link between a document
and the organisation that published it (i.e. organisation publishes document), or the
link between a map and the geographic region it depicts (i.e. map depicts geographic
region). In RDF relationships are encoded as object type properties.
Slide 51
DATASUPPORTOPEN
Examples of classes, relationships and properties:
The Core Person Vocabulary in UML
52
class Healthcare Domain
Core Vocabularies::Identifier
dateOfIssue :dateTime [0..1]
identifier :string [1..1]
identifierType :string [0..1]
issuingAuthority :string [0..1]
issuingAuthorityUri :URI [0..1]
Core Vocabularies::Person
alternativeName :string
birthName :string
dateOfBirth :dateTime
dateOfDeath :dateTime
familyName :string
fullName :string
gender :code
givenName :string
patronymicName :string
Core Vocabularies::Location
geographicIdentifier :URI
geographicName :string
Core Vocabularies::Address
addressArea :string
addressID :string
adminUnitL1 :string
adminUnitL2 :string
fullAddress :string
locatorDesignator :string
locatorName :string
poBox :string
postCode :string
postName :string
thoroughfare :string
Core Vocabularies::Geometry
lat :string
long :string
wkt :string
xmlGeometry :XML
address
identifies
geometry
placeOfDeath
countryOfDeath
placeOfBirth
countryOfBirth
identifier
UML: The Unified Modelling Language class diagrams provide the means for expressing
the conceptual data model of vocabularies, such as the ISA Core Vocabularies, thus
facilitating the understanding of the meaning of the data model.
Relationships Class
Properties
Class
Class
Class
Class
DATASUPPORTOPEN
Introduction to SPARQL
The RDF Query Language
Slide 53
DATASUPPORTOPEN
About SPARQL
SPARQL is the standard language to query graph data represented as
RDF triples.
• SPARQL Protocol and RDF Query Language
• One of the three core standards of the Semantic Web, along with RDF
and OWL.
• Became a W3C standard January 2008.
• SPARQL 1.1 is a W3C Recommendation since March 2013.
Slide 54
DATASUPPORTOPEN
Types of SPARQL queries
• SELECT. Return a table of all X, Y, etc. satisfying the following
conditions ...
• CONSTRUCT. Find all X, Y, etc. satisfying the following conditions
... and substitute them into the following template in order to
generate (possibly new) RDF statements, creating a new graph.
• DESCRIBE. Find all statements in the dataset that provide
information about the following resource(s) ... (identified by name or
description)
• INSERT. Add triples to the RDF graph.
• DELETE. Delete triples from the RDF graph.
• ASK. Are there any X, Y, etc. satisfying the following conditions ...
Slide 55
See also:
http://www.euclid-project.eu/modules/chapter2
https://joinup.ec.europa.eu/community/ods/document/tm13-introduction-rdf-sparql-en
DATASUPPORTOPEN
PREFIX dct: <http://purl.org/dc/terms/>
PREFIX dcat: <http://www.w3.org/TR/vocab-dcat/>
SELECT ?title
WHERE
{
?dataset rdf:type dcat:Dataset .
?dataset rdf:title ?title
}
Structure of a SPARQL Query
Slide 56
Type of
query Variables, i.e. what to search for
RDF triple patterns, i.e.
the conditions that
have to be met
Definition of
prefixes
DATASUPPORTOPEN
SELECT – return the name of a dataset with
particular URI
Slide 57
<http://.../authority/file-type/> rdf:type dcat:Dataset.
<http://.../authority/file-type/> dct:title “File types Name Authority List“ .
<http://.../authority/file-type/> dct:publisher < http://open-data.europa.eu/en/data/publisher/publ>.
< http://.../publisher/publ> rdf:type dct:Agent .
< http://.../publisher/publ> dct:title “Publications Office” .
PREFIX dcat: <http://www.w3.org/TR/vocab-dcat/>
PREFIX dct: <http://purl.org/dc/terms/>
SELECT ?dataset
WHERE
{
<http://.../authority/file-type/> dct:title ?dataset .
}
dataset
“File types Name Authority List”
Sample data
Query
Result
DATASUPPORTOPEN
SELECT - return the name and publisher of a
dataset
Slide 58
PREFIX dcat: <http://www.w3.org/TR/vocab-dcat/>
PREFIX dct: <http://purl.org/dc/terms/>
SELECT ?dataset ?publisher
WHERE
{http://.../authority/file-type/ dct:publisher ?publisherURI.
http://.../authority/file-type/ dct:title ?dataset.
?publisherURI dct:title ?publisher . }
dataset publisher
“File types Name Authority List” “Publications Office”
<http://.../authority/file-type/> rdf:type dcat:Dataset.
<http://.../authority/file-type/> dct:title “File types Name Authority List“ .
<http://.../authority/file-type/> dct:publisher < http://open-data.europa.eu/en/data/publisher/publ>.
< http://.../publisher/publ> rdf:type dct:Agent .
< http://.../publisher/publ> dct:title “Publications Office” .
Sample data
Query
Result
DATASUPPORTOPEN
SPARQL Example – EU ODP (1)
Slide 59
DATASUPPORTOPEN
SPARQL Example – EU ODP (2)
Slide 60
DATASUPPORTOPEN
SPARQL Example – EU ODP (2)
Slide 61
DATASUPPORTOPEN
Summary
• RDF is a general way to express data intended for publishing on the
Web.
• RDF data is expressed in triples: subject, predicate, object.
• Different syntaxes exist for expressing data in RDF.
• SPARQL is a standardised language to query graph data expressed
as RDF.
• SPARQL can be used to query and update RDF data.
Slide 62
DATASUPPORTOPEN Slide 63
Learning Module 3:
Workshop for Publishing Open
Linked EU Data
DATASUPPORTOPEN
Workshop for publishing open linked EU data
This module is about...
• Creating an RDF vocabulary for modelling your data.
 How to reuse existing vocabularies to model your data.
 How to create new classes and properties in RDF.
 How and where to publish your RDF vocabulary so that it can be reused
by others.
• An example of how tabular data can be published as Linked Open
Data using Open Refine.
Slide 64
DATASUPPORTOPEN
Learning objectives
By the end of this training module you should have an understanding
of:
• What the best practices are for creating an RDF vocabulary for
modelling your data.
• Where to find RDF vocabularies for reuse.
• How you can create your own RDF vocabulary.
• How to publish your RDF vocabulary.
• The process and methodology for developing semantic agreements
developed by the ISA Programme of the European Commission.
Slide 65
DATASUPPORTOPEN
Creating an RDF vocabulary
How to reuse other vocabularies, define your own terms,
publish and promote your vocabulary
Slide 66
DATASUPPORTOPEN
6 steps for creating an RDF vocabulary
Start with a robust Domain Model developed following a
structured process and methodology.
Research existing terms and their usage and maximise reuse
of those terms.
Where new terms can be seen as specialisations of existing terms,
create sub class and sub properties.
Where new terms are required, create them following
commonly agreed best practice.
Publish within a highly stable environment designed to be
persistent.
Publicise the RDF vocabulary by registering it with relevant
services.
Slide 67
1
See also:
https://joinup.ec.europa.eu/community/semic/document/cookbook-
translating-data-models-rdf-schemas
2
3
4
5
6
DATASUPPORTOPEN
Start with a robust Domain Model
Slide 68
1
has
Ceiling
has
Political
category
Amount
Currency
Figure
Type
Year
Nomenclature
Type
Heading
EU Programme
Code
Type
Political category
Code
Description
Corporate body
Code
Type
Location
Introduction
Remark
Conditions
Acronym
Legal base period
Legal base type
Legal base status
has
Corporate body
has
Nomenclature
has
EU Programme
DATASUPPORTOPEN
General purpose vocabularies: DCMI, RDFS
To name things: rdfs:label, foaf:name, skos:prefLabel
To describe people: FOAF, vCard, Core Person Vocabulary
To describe projects: DOAP, ADMS.SW
To describe interoperability assets: ADMS
To describe registered organisations: Registered Organisation Vocabulary
To describe addresses: vCard, Core Location Vocabulary
To describe public services: Core Public Service Vocabulary
To describe datasets: DCAT, DCAT Application Profile, VoID
Reuse existing terms and vocabularies
Slide 69
2
DATASUPPORTOPEN
Well-known vocabularies:
Slide 70
DCAT-AP Vocabulary for describing datasets in Europe
Core Person Vocabulary
Vocabulary to describe the fundamental characteristics of
a person, e.g. the name, the gender, the date of birth...
DOAP Vocabulary for describing projects
ADMS Vocabulary for describing interoperability assets.
Dublin Core Defines general metadata attributes
Registered Organisation Vocabulary
Vocabulary for describing organizations, typically in a
national or regional register
Organization Ontology for describing the structure of organizations
Core Location Vocabulary
Vocabulary capturing the fundamental characteristics of a
location.
Core Public Service Vocabulary
Vocabulary capturing the fundamental characteristics of a
service offered by public administration
schema.org
Agreed vocabularies for publishing structured data on the
Web elaborated by Google, Yahoo and Microsoft
See also:
http://www.w3.org/wiki/TaskForces/CommunityProj
ects/LinkingOpenData/CommonVocabulariesReuse existing terms and
vocabularies
2
DATASUPPORTOPEN
• Reuse greatly aids interoperability of your data
 Use of dcterms:created, for example, the value for which should be a data typed
date such as 2013-02-21^^xsd:date, is immediately processable by many machines.
If your schema encourages data publishers to use a different term and date format,
such as ex:date "21 February 2013" – data published using your schema will require
further processing to make it the same as everyone else's.
• Reuse adds credibility to your schema.
 It shows it has been published with care and professionalism, again, this promotes
its reuse.
• Reuse is easier and cheaper.
 Reusing classes and properties from well defined and properly hosted vocabularies
avoids your having to replicate that effort.
Slide 71
Advantages of reuse:
Reuse existing terms and vocabularies2
DATASUPPORTOPEN
You can find reusable RDF vocabularies on:
Slide 72
http://joinup.ec.europa.eu/ http://lov.okfn.org/
Reuse existing terms and vocabularies2
DATASUPPORTOPEN
Creation of sub-classes and sub-properties
• RDF schemas and vocabularies often include terms that are very
generic.
• By creating sub-class and sub-property relationships, systems
that understand the super property or super class may be able to
interpret the data even if the more specific terms are unknown.
• Do not create sub-classes and sub-properties simply to
allow you to use your own term for something that already
exists.
Slide 73
3
DATASUPPORTOPEN
Creation of sub-classes and sub-properties
Slide 74
3
The EU Budget vocabulary defines the introduction property as a sub-
property of dct:description.
Nomenclature
Type
Heading
Introduction
Remark
Conditions
DATASUPPORTOPEN
Creation of sub-classes and sub-properties
Slide 75
3
The EU Budget vocabulary defines the has nomenclature property as a
sub-property of dct:subject.
Amount
Currency
Figure
Type
Year
Nomenclature
Type
Heading
Introduction
Remark
Conditions
has
Nomenclature
DATASUPPORTOPEN
Where new terms are required, create them
following commonly agreed best practices
 Classes begin with a capital letter and are always singular, e.g.
skos:Concept.
 Properties begin with a lower case letter, e.g. rdfs:label.
 Object properties should be verbs, e.g. org:hasSite.
 Data type properties should be nouns, e.g. dcterms:description.
 Use camel case if a term has more than one word, e.g.
foaf:isPrimaryTopicOf.
Slide 76
4
DATASUPPORTOPEN
Where new terms are required, create them
following commonly agreed best practices
If there is no suitable authoritative reusable vocabulary for describing your data, use
conventions for describing your own vocabulary:
- RDF Schema (RDFS)
- Web Ontology Language (OWL)
Example: defining the “Amount” class
Slide 77
4
See also:
http://www.slideshare.net/OpenDataSupport/model-your-
data-metadata
Amount
Currency
Figure
Type
Year
DATASUPPORTOPEN
Where new terms are required, create them
following commonly agreed best practices
If there is no suitable authoritative reusable vocabulary for describing your data, use
conventions for describing your own vocabulary:
- RDF Schema (RDFS)
- Web Ontology Language (OWL)
Example: defining the “amount type” property
Slide 78
4
See also:
http://www.slideshare.net/OpenDataSupport/model-your-
data-metadata
Amount
Currency
Figure
Type
Year
DATASUPPORTOPEN
Where new terms are required, create them
following commonly agreed best practices
When defining new properties, consider to define their domain and range.
 A range states that the values of a property are instances of one or more classes.
 A domain states on which classes a given property can be used.
Slide 79
4
See also:
http://www.slideshare.net/OpenDataSupport/model-your-
data-metadata
has
Ceiling
Amount
Currency
Figure
Type
Year
Political category
Code
Description
DATASUPPORTOPEN
Publish within a highly stable environment
designed to be persistent
• Choose a stable namespace for your RDF vocabulary
 Example: http://data.europa.eu/bud/
• Use good practices on the publication of persistent Uniform
Resource Identifiers (URI) sets, both in terms of format, design rules
and management.
 Examples:
o http://www.w3.org/ns/adms
o http://purl.org/dc/elements/1.1
Slide 80
5
See also:
https://joinup.ec.europa.eu/community/semic/document/cookbook-translating-
data-models-rdf-schemas
http://www.slideshare.net/OpenDataSupport/design-and-manage-persitent-uris
DATASUPPORTOPEN
Publicise the RDF vocabulary by registering it
with relevant services
Once your RDF vocabulary is published you will want people to know about it.
To reach a wider audience, register it on Joinup and Linked Open Vocabularies.
Slide 81
6
DATASUPPORTOPEN
Conclusions
Slide 82
Start with a robust Domain Model developed following a structured
process and methodology.
Research existing terms and their usage and maximise reuse of those
terms.
Where new terms can be seen as specialisations of existing terms, create
sub class and sub properties as appropriate.
Where new terms are required, create them following commonly agreed
best practice in terms of naming conventions etc
Publish within a highly stable environment designed to be persistent.
Publicise the RDF vocabulary by registering it with relevant services.
Analyse
Model
Publish
DATASUPPORTOPEN
Example
Using Open Refine for RDF to publish tabular data as
Linked Data.
Slide 83
DATASUPPORTOPEN
What is Open Refine
Slide 84
“OpenRefine is a powerful tool for working with messy data, cleaning it,
transforming it from one format into another, ...”
- openRefine.org
See also:
Open Refine website
http://openrefine.org/
DATASUPPORTOPEN
What is Open Refine RDF extension
Open Refine RDF extension allows you to easily import data in different
formats such as :
 CSV;
 Excel(.xls and .xlsx);
 JSON;
 XML; and
 RDF/XML.
And then determine the intended structure of an RDF dataset, by
drawing a template graph.
Slide 85
See also:
LOD 2 Webinar – Open Refine
http://www.youtube.com/watch?v=4Ve93C238gI
DATASUPPORTOPEN
Using Open Refine to model and publish open data
Getting started
1. Install Open Refine from: https://github.com/OpenRefine
2. Install the RDF extension : http://refine.deri.ie/
And then...
Describe your data in a spreadsheet.
Create a project and upload it in Open Refine.
Clean up the data
Map your data to appropriate RDF classes & properties.
Export the data in RDF.
Slide 86
1
2
3
4
5
DATASUPPORTOPEN
Example situation
Publish statistical data as RDF according to RDF
Data Cube Vocabulary
Digital Agenda Scoreboard
Slide 87
DATASUPPORTOPEN
Describe your data in a spreadsheet
Download the tabular data
Slide 88
1
DATASUPPORTOPEN
Create a project and upload it in Open Refine
Slide 89
2
Upload the
spreadsheet
Select relevant
tabs
Create the
project
DATASUPPORTOPEN
Clean up the data – table harmonisation
Slide 90
3
• Star & remove unnessary rows
• Rename columns
• Use facets to select the data to
be published
DATASUPPORTOPEN
Clean up the data – prepare RDF
Slide 91
3
• Create URI representation for
the involved object values
• via formula
• via reconsiliation
DATASUPPORTOPEN
Map your data to appropriate RDF classes &
properties (model your data)
Slide 92
4
Understand the target vocabulary: e.g. W3C RDF Data Cube Vocabulary
DATASUPPORTOPEN
Map your data to appropriate RDF classes &
properties (model your data)
Slide 93
4
Define a skeleton to
transform your spreadsheet
data to RDF
DATASUPPORTOPEN
Map your data to appropriate RDF classes &
properties (model your data)
You can map the data to the ontology using a simple graphical interface to
create or edit an existing RDF skeleton.
You can set the base URI for the data.
Slide 94
Graphical interface to copy/paste an
existing RDF skeleton
Graphical interface to edit an
RDF skeleton
4
DATASUPPORTOPEN
Export your data to RDF/XML or Turtle
Slide 95
5
Export of the data
in Turtle
DATASUPPORTOPEN
Production pipelines
From desk to automated pipeline
Slide 96
flexibility
volume
OpenRefine
UnifiedViews
Cellar
DATASUPPORTOPEN
Thank you for your attention!
...and now YOUR questions?
Slide 97
DATASUPPORTOPEN
References
• 5 ★ Open Data. http://5stardata.info/
• ADMS Brochure. ISA Programme.
https://joinup.ec.europa.eu/elibrary/document/adms-brochure
• An organization ontology. W3C. http://www.w3.org/TR/vocab-
org/ W3C.
• Case study on how Linked Data is transforming eGovernment.
ISA Programme.
https://joinup.ec.europa.eu/community/semic/document/case-
study-how-linked-data-transforming-egovernment
• Common Vocabularies / Ontologies / Micromodels. W3C.
http://www.w3.org/wiki/TaskForces/CommunityProjects/Linki
ngOpenData/CommonVocabularies
• Cookbook for translating Data Models to RDF Schemas. ISA
Programme.
https://joinup.ec.europa.eu/community/semic/document/cookb
ook-translating-data-models-rdf-schemas
• D7.1.3 - Study on persistent URIs, with identification of best
practices and recommendations on the topic for the MSs and the
EC. ISA Programme.
https://joinup.ec.europa.eu/sites/default/files/D7.1.3%20-
%20Study%20on%20persistent%20URIs.pdf
• EUCLID. Course 1: Introduction and Application Scenarios.
http://www.euclid-project.eu/modules/course1
• Linked Data. Tim Berners-Lee.
http://www.w3.org/DesignIssues/LinkedData.html
Slide 98
• Linked Data Cookbook. W3C.
http://www.w3.org/2011/gld/wiki/Linked_Data_Cookbook
• Linking Open Data cloud diagram, by Richard Cyganiak and Anja
Jentzsch. http://lod-cloud.net/
• Module 2: Querying Linked Data. EUCLID. http://www.euclid-
project.eu/modules/course2
• Open Data – An Introduction. The Open Knowledge Foundation.
http://okfn.org/opendata/
• Open Refine: https://github.com/OpenRefine
• RDF Extension: http://refine.deri.ie/
• Resource Description Framework. W3C.
http://www.w3.org/RDF/
• Semantic Web Stack. W3C.
http://www.w3.org/DesignIssues/diagrams/sweb-
stack/2006a.png
• SPARQL Query Language for RDF. W3C.
http://www.w3.org/TR/rdf-sparql-query/
DATASUPPORTOPEN
Further reading
Slide 99
EC ISA, Process and methodology for developing semantic
agreements,
https://joinup.ec.europa.eu/community/core_vocabularies/documen
t/process-and-methodology-developing-semantic-agreements
EC ISA, Cookbook for translating Data Models to RDF Schemas
https://joinup.ec.europa.eu/community/semic/document/cookbook-
translating-data-models-rdf-schemas
DATASUPPORTOPEN
Further reading
EUCLID - Course 1: Introduction and Application Scenarios
http://www.euclid-project.eu/modules/course1
EUCLID - Course 2: Querying Linked Data
http://www.euclid-project.eu/modules/course2
Learning SPARQL. Bob DuCharme.
http://www.learningsparql.com/
Linked Data Cookbook, W3C Government Linked Data Working
Group
http://www.w3.org/2011/gld/wiki/Linked_Data_Cookbook
Slide 100
DATASUPPORTOPEN
Further reading
Linked Data: Evolving the Web into a Global Data Space. Tom Heath
and Christian Bizer.
http://linkeddatabook.com/editions/1.0/
Linked Open Data: The Essentials. Florian Bauer, Martin Kaltenböck.
http://www.semantic-web.at/LOD-TheEssentials.pdf
Linked Open Government Data. Li Ding Qualcomm, Vassilios
Peristeras and Michael Hausenblas.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6237454
Semantic Web for the working ontologist. Dean Allemang, Jim
Hendler.
http://workingontologist.org/
Slide 101
DATASUPPORTOPEN
Be part of our team...
Slide 102
Find us on
Contact us
Join us on
Follow us
Open Data Support
http://www.slideshare.net/OpenDataSupport
http://www.opendatasupport.euOpen Data Support
http://goo.gl/y9ZZI
@OpenDataSupport contact@opendatasupport.eu
DATASUPPORTOPEN
This presentation has been created by PwC
Authors:
Michiel De Keyzer, Nikolaos Loutas, Jana
Makedonska, Brecht Wyns
Presentation
metadata
Slide 103
Open Data Support is funded by
the European Commission
under SMART 2012/0107 ‘Lot
2: Provision of services for the
Publication, Access and Reuse of
Open Public Data across the
European Union, through
existing open data
portals’(Contract No. 30-CE-
0530965/00-17).
© 2015 European Commission
Disclaimers
1. The views expressed in this presentation are purely those of the authors and may not, in any
circumstances, be interpreted as stating an official position of the European Commission.
The European Commission does not guarantee the accuracy of the information included in this
presentation, nor does it accept any responsibility for any use thereof.
Reference herein to any specific products, specifications, process, or service by trade name,
trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement,
recommendation, or favouring by the European Commission.
All care has been taken by the author to ensure that s/he has obtained, where necessary,
permission to use any parts of manuscripts including illustrations, maps, and graphs, on which
intellectual property rights already exist from the titular holder(s) of such rights or from her/his
or their legal representative.
2. This presentation has been carefully compiled by PwC, but no representation is made or
warranty given (either express or implied) as to the completeness or accuracy of the information it
contains. PwC is not liable for the information in this presentation or any decision or
consequence based on the use of it.. PwC will not be liable for any damages arising from the use of
the information contained in this presentation. The information contained in this presentation is
of a general nature and is solely for guidance on matters of general interest. This presentation is
not a substitute for professional advice on any particular matter. No reader should act on the basis
of any matter contained in this publication without considering appropriate professional advice.

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Llinked open data training for EU institutions

  • 1. DATA SUPPORT OPEN Linked Open Data Principles, Technologies and Examples PwC firms help organisations and individuals create the value they’re looking for. We’re a network of firms in 158 countries with close to 180,000 people who are committed to delivering quality in assurance, tax and advisory services. Tell us what matters to you and find out more by visiting us at www.pwc.com. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details.
  • 2. DATASUPPORTOPEN Learning objectives By the end of the course, participants should have a clear understanding of: • What linked open data is; • What is the difference between linked and open data; • How to publish linked data; • The economic and social aspects of linked data; • How linked data technologies can be applied to improve the availability, understandability and usability of EU data. Slide 2
  • 3. DATASUPPORTOPEN Content This training consists of 3 modules: 1. Introduction to linked data 2. Introduction to RDF & SPARQL 3. Workshop on publishing open linked EU data Slide 3
  • 5. DATASUPPORTOPEN Introduction to linked data This module contains ... • An introduction to the linked data principles; • The expected benefits of linked data; • An introduction to linked data technologies; • An outline of the 5-star scheme for publishing linked data; • An overview of linked data initiatives in Europe. Slide 5 Find more on: training.opendatasupport.eu
  • 6. DATASUPPORTOPEN What is linked data? Evolution from a document-based Web to a Web of interlinked data. Slide 6
  • 7. DATASUPPORTOPEN The Web is evolving from a “Web of linked documents” into a “Web of linked data” • The Web started as a collection of documents published online – accessible at a Web location identified by a URL. • These documents often contain data about real-world resources which is mainly human-readable and cannot be understood by machines. • The Web of Data is about enabling the access to this data, by making it available in machine-readable formats and connecting it using Uniform Resource Identifiers (URIs), thus enabling people and machines to collect the data, and put it together to do all kinds of things with it (permitted by the licence). Machine-readable data (or metadata) is data in a format that can be interpreted by a computer. 2 types of machine-readable data exist: • human-readable data that is marked up so that it can also be understood by computers, e.g. microformats, RDFa; • data formats intended principally for computers, e.g. RDF, XML and JSON. Slide 7 See also: http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html http://linkeddatabook.com/editions/1.0/
  • 8. DATASUPPORTOPEN Defining linked data Providing data as a service “Linked data is a set of design principles for sharing machine-readable data on the Web for use by public administrations, business and citizens.” EC ISA Case Study: How Linked Data is transforming eGovernment The four design principles of Linked Data (by Tim Berners Lee): 1. Use Uniform Resource Identifiers (URIs) as names for things. 2. Use HTTP URIs so that people 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 they can discover more things. Slide 8 See also: http://www.youtube.com/watch?v=4x_xzT5eF5Q http://www.w3.org/DesignIssues/LinkedData.html http://www.youtube.com/watch?v=uju4wT9uBIA
  • 9. DATASUPPORTOPEN The value proposition of linked (open) government data • Flexible data integration: facilitates data integration and enables the interconnection of previously disparate government datasets. • Efficiency gains in data integration– the network effect: the addition of each new dataset increases the value of those datasets that are already published! • Ease of navigation: makes browsing through complex data easier via URIs. • Increase in data quality:  The use of URIs leads to improved data management and quality.  The increased (re)use triggers a growing demand to improve data quality. Through crowd-sourcing and self-service mechanisms, errors are progressively corrected. 9 See also: ISA Study on Business Models for LOGD https://joinup.ec.europa.eu/community/semic/document/study- business-models-linked-open-government-data-bm4logd
  • 10. DATASUPPORTOPEN The value proposition of linked (open) government data • Increase in data usability by providing data as a service:  Resolvable URIs  Data is available in different formats, not limited to RDF, e.g. XML, CSV, text, JSON… • Compatible with existing standards and technologies: a linked data infrastructure can provide access to homogenised, linked, and enriched data using standard Web-based interfaces (such as HTTP and SPARQL) and Web-based languages (such as XHTML, RDF+XML), on top of either:  Existing relational/spatial database systems, by applying database-to-RDF conversions; or  Existing XML/file-based data. 10
  • 11. DATASUPPORTOPEN The value proposition of linked (open) government data • Ease of model updates: RDF data models and vocabularies can be extended, adapted and updated more easily. Changes can be reflected on the data with lower costs and effort (compared to traditional relational databases). • Cost reduction: The reuse of LOGD in e-Government applications leads to considerable cost reductions, when it comes to service integration, data use, reuse, and exchange. • New services: The availability of LOGD gives rise to new, integrated, services offered by the public and/or private sector. 11 See also: ISA Study on Business Models for LOGD https://joinup.ec.europa.eu/community/semic/document/study- business-models-linked-open-government-data-bm4logd
  • 12. DATASUPPORTOPEN The four principles of linked data in practice 1. Use Uniform Resource Identifiers (URIs) as names for things. 2. Use HTTP URIs so that people can look up those names. E.g. for an organisation: UNICEF in EuroVoc. - http://eurovoc.europa.eu/1022 Slide 12
  • 13. DATASUPPORTOPEN The four principles in practice 3. When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL). 4. Include links to other URIs so that people/machines can discover more things. Slide 13
  • 14. DATASUPPORTOPEN Linked data vs. open data Open data Data can be published and be publicly available under an open licence without linking to other data sources. Linked data Data can be linked to URIs from other data sources, using open standards such as RDF without being publicly available under an open licence. Slide 14 “Open data is data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and share- alike.” - OpenDefinition.org See also: Cobden et al., A research agenda for Linked Closed Data http://ceur-ws.org/Vol-782/CobdenEtAl_COLD2011.pdf
  • 15. DATASUPPORTOPEN Linked data foundations URIs for naming things, RDF for describing data and SPARQL for querying linked data. Slide 15
  • 16. DATASUPPORTOPEN Uniform Resource Identifier (URI) “A Uniform Resource Identifier (URI) is a compact sequence of characters that identifies an abstract or physical resource.” – ISA’s 10 Rules for Persistent URIs A country, e.g. Belgium - http://publications.europa.eu/resource/authority/country/BEL An organisation, e.g. the Publications Office - http://publications.europa.eu/resource/authority/corporate-body/PUBL A dataset, e.g. Countries Named Authority List - http://publications.europa.eu/resource/authority/country/ Slide 16 BE See also: http://www.slideshare.net/OpenDataSupport/design -and-manage-persitent-uris
  • 17. DATASUPPORTOPEN RDF & SPARQL The Resource Description Framework (RDF ) is a syntax for representing data and resources on the Web Slide 17 RDF breaks every piece of information down in triples: • Subject – a resource, which is identified with a URI. • Predicate – a URI-identified reused specification of the relationship. • Object – a resource or literal to which the subject is related. SPARQL is a standardised language for querying RDF data. http://example.org/place/Brussels is the capital of “Belgium”. OR http://example.org/place/Brussels is the capital of http://example.org/place/Belgium. Subject Predicate Object See also: http://www.slideshare.net/OpenDataSupport/introduction-to-rdf-sparql
  • 18. DATASUPPORTOPEN How to publish linked data? Paving the way towards 5-star linked data Slide 18
  • 19. DATASUPPORTOPEN 5 star-schema of Linked Open Data ★ Make your stuff available on the Web (whatever format) under an open license. ★★ Make it available as structured data (e.g., Excel instead of image scan of a table). ★★★ Use non-proprietary formats (e.g., CSV instead of Excel). ★★★★ Use URIs to denote things, so that people can point at your stuff. ★★★★★ Link your data to other data to provide context. Slide 19
  • 20. DATASUPPORTOPEN ★ Make your stuff available on the Web under an open licence Slide 20 Trends, risks and vulnerabilities in securities markets
  • 21. DATASUPPORTOPEN ★ ★ Make it available as structured data Slide 21 Waterbase - Emissions to water: CountryCode
  • 22. DATASUPPORTOPEN ★ ★ ★ Use non-proprietary formats • Proprietary: Excel, Word, PDF... • Non-proprietary: XML, CSV, RDF, JSON, ODF... DG Enlargement - Regional programmes: Slide 22
  • 23. DATASUPPORTOPEN ★ ★ ★ ★ Use URIs to denote things Slide 23 See also: http://www.slideshare.net/OpenDataSupport/design-and-manage-persitent-uris Food Additives - http://open-data.europa.eu/en/data/dataset/1gXgb0Yj73R4ttDChQ5Wyg
  • 24. DATASUPPORTOPEN ★ ★ ★ ★ ★ Link your data to other data to provide context Slide 24 Corporate bodies Named Authority Lists - http://open-data.europa.eu/en/data/dataset/corporate-body
  • 25. DATASUPPORTOPEN LOGD roadblocks • Necessary investments. • Lack of necessary competencies. • Perceived lack of tools. • Lack of service level guarantees. • Missing, restrictive, or incompatible licences. • Surfeit of standard vocabularies. • The inertia of the status quo – change is accomplished slowly. 25 See also: ISA Study on Business Models for LOGD https://joinup.ec.europa.eu/community/semic/document/study-business- models-linked-open-government-data-bm4logd
  • 26. DATASUPPORTOPEN Linked data initiatives in Europe Examples on supra-national, national, regional and private initiatives in the area of linked data. Slide 26
  • 27. DATASUPPORTOPEN EU institutions initiatives – some examples • European Environment Agency SPARQL endpoint: Tool allowing searching for linked data published by the the European Environment Agency. http://semantic.eea.europa.eu/sparql • EU Open Data Portal SPARQL endpoint: Allows searching for linked metadata of datasets published on the EU Open Data Portal. https://open-data.europa.eu/en/linked-data • Publications Office of the EU - CELLAR SPARQL Endpoint: Allows searching for linked data published by the Publications Office of the EU, such as legislation data, publications data, etc. http://publications.europa.eu/en/linked-data • DG SANTE SPARQL endpoint: Tool for querying linked open data on European Community Health Indicators, the EU Register of Health Claims, etc. http://ec.europa.eu/semantic_webgate/ • Europeana SPARQL endpoint: Tool allowing querying a multi-lingual online collection of millions of digitized items from European museums, libraries, archives and multi-media collections. http://labs.europeana.eu/api/linked-open-data-SPARQL-endpoint Slide 27
  • 28. DATASUPPORTOPEN Initiatives funded by the European Commission Slide 28 ADMS. SW CORE VOCABULARY PUBLIC SERVICE
  • 29. DATASUPPORTOPEN Member State initiatives – some examples DE – Bibliotheksverbund Bayern Linked data from 180 academic libraries in Bavaria, Berlin and Brandenburg. IT – Agenzia per l’Italia digitiale Three datasets published as linked data: the Index of Public Administration, the SPC contracts for web services and conduction systems and the Classifications for the data in Public Administration. NL – Building and address register The Dutch Address and Buildings base register published as linked data. UK – Ordnance Survey Three OS Open Data products published as linked data: the 1:50 000 Scale Gazetteer, Code-Point Open and the administrative geography taken from Boundary Line. UK – Companies House Publishing basic company details as linked data using a simple URI for each company in their database. Slide 29 See also: ISA Study on Business Models for LOGD https://joinup.ec.europa.eu/community/semic/document/study-business- models-linked-open-government-data-bm4logd
  • 30. DATASUPPORTOPEN Member States Initiatives – UK National Archives Slide 30 Three considerations for legislation as data • Typographic layout • Versioning / changes over time • Semantics Semantic representation using RDF and Linked Data • URIs for things & RDF data model Requires granular URIs to name things • Identifier: http://www.legislation.gov.uk/id/{type}/{year}/{number}/section/{number} • Representation: .../data.[xml | xht | pdf | rdf | feed] Source: https://www.nationalarchives.gov.uk/documents/information-management/open-and-linked-data-johnsheridan.ppt See also: The European Legal Identifier (ELI) http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=URISERV:jl0068
  • 31. DATASUPPORTOPEN Member States Initiatives – UK National Archives Slide 31 Versioning of legislation in RDF http://www.legislation.gov.uk/id/ukpga/2010/32/section/12/4/data.rdf
  • 32. DATASUPPORTOPEN Open & linked data at BBC • BBC Things, the open data website of BBC, allows anyone to access the data that BBC stores about data on the places, people and organisations that appear in BBC programmes and online content. • This data already powers large parts of the BBC website, including BBC News and Sport. • BBC Things is part of the BBC Linked Data Platform, which provides public access to data stored in the BBC platform and provides a public reference for all of the things that the BBC creates content about. Slide 32 Further reading: http://www.bbc.co.uk/things/search?q=juncker http://www.forbes.com/sites/bernardmarr/2015/06/01/how-big-data-drives-success-at-rolls-royce/
  • 33. DATASUPPORTOPEN Slide 33 Open & linked data at BBC
  • 34. DATASUPPORTOPEN Data Value Chains using Linked Data at Volkswagen Slide 34 Source: Sören Auer, SEMIC Conference 2015, Riga https://joinup.ec.europa.eu/sites/default/files/isa_field_path/presentation_by_soren_auer_-_creating_data_value_chains_by_linking_enterprise_data.pdf
  • 35. DATASUPPORTOPEN 1. Link databases “I’d like to know last month’s production volume total for all fields in which GDF Suez E&P Norge is a licensee.” • Data spread across multiple databases: GDF Suez data + Norwegian Government data + Wikipedia data • Need to uniquely identify resources. 2. Add meaning “I’d like to know all fields operated by Statoil Petroleum AS with their production volumes.” • Need for adding semantics in order to allow machine reasoning. For example: • Kristin is a field • Åsgard is an oil platform • Statoil Petroleum AS is a company Linked Data in the oil and gas industry Slide 35 Further reading: http://www.topquadrant.com/ resources/solutions/docs/Se mantic-data-oil-and-gas.pdf
  • 36. DATASUPPORTOPEN Conclusions • Linked data is a set of design principles for sharing machine-readable data on the Web. • URIs, RDF and SPARQL form the foundational layer for Linked data. • Linked data offers a number of advantages such as: o Data integration with small impact on legacy systems; o Enables for semantic interoperability; o Easier browsing through complex data; o Increased data quality; Slide 36
  • 37. DATASUPPORTOPEN Conclusions cont’d • Linked data offers a number of advantages such as: o Enables easy updates, adaptations and extensions of data models; o Cost reduction from the reuse of LOGD in e-Government applications; o Enables creativity and innovation through context and knowledge- creation. Slide 37
  • 39. DATASUPPORTOPEN Introduction to RDF and SPARQL This module contains ... • An introduction to the Resource Description Framework (RDF) for describing your data. • An introduction to SPARQL on how you can query and manipulate data in RDF. Slide 39 Find more on: training.opendatasupport.eu
  • 40. DATASUPPORTOPEN Learning objectives By the end of this training module you should have a clear understanding of: • The Resource Description Framework (RDF); • How to write/read RDF; • How you can describe your data with RDF; • What SPARQL is; • How to understand and write a SPARQL SELECT query. Slide 40
  • 41. DATASUPPORTOPEN Resource Description Framework An introduction to RDF. Slide 41
  • 42. DATASUPPORTOPEN RDF in the stack of Semantic Web technologies Resource: Everything that can have a unique identifier (URI), e.g. pages, places, people, organisations, products... Description: attributes, features, and relations of the resources Framework: model, languages and syntaxes for these descriptions • Published as a W3C recommendation in 1999. • RDF was originally introduced as a data model for metadata. • RDF was generalised to cover knowledge of all kinds. Slide 42
  • 43. DATASUPPORTOPEN Example: RDF description of an organisation Publications Office, 2, rue Mercier, 2985 Luxembourg, LUXEMBOURG Slide 43 <rdf:RDF xmlns:rdfs=“http://www.w3.org/2000/01/rdf-schema#” xmlns:org=“http://www.w3.org/ns/org#” xmlns:locn=“http://www.w3.org/ns/locn#” > <org:Organization rdf:about=“http://publications.europa.eu/resource/authority/corporate- body/PUBL”> <rdfs:label> “Publications Office”< /rdfs:label> <org:hasSite rdf:resource=“http://example.com/site/1234”/> </org:Organization> <locn:Address rdf:about=“http://example.com/site/1234”/> <locn:fullAddress>”2, rue Mercier, 2985 Luxembourg, LUXEMBOURG”</locn:fullAddress> </locn:Address> </rdf:RDF>
  • 45. DATASUPPORTOPEN What is a triple? Slide 45 Every piece of information expressed in RDF is represented as a triple: • Subject – a resource, which is identified with a URI. • Predicate – a URI-identified reused specification of the relationship. • Object – a resource or literal to which the subject is related. http://publications.europa.eu/resource/authority/file-type/ has a title “File types Name Authority List”. Subject Predicate Object Example: name of a dataset:
  • 46. DATASUPPORTOPEN RDF Syntax RDF/XML Slide 46 <rdf:RDF xmlns:dcat=“http://www.w3.org/TR/vocab-dcat/“ xmlns:dct=“http://purl.org/dc/terms/” <dcat:Dataset rdf:about=“http://publications.europa.eu/resource/authority/file-type/”> <dct:title> “File types Named Authority List”< /dct:title> <dct:publisher rdf:resource=“http://open-data.europa.eu/en/data/publisher/publ”/> </dcat:Dataset> <dct:Agent rdf:about=“http://open-data.europa.eu/en/data/publisher/publ”/> <dct:title>”Publications Office”</dct:title> </dct:Publisher> </rdf:RDF> Subject Predicate Object Graph Definition of prefixes Description of data – triples
  • 47. DATASUPPORTOPEN Visual representation (RDF graph) of the triples from the RDF/XML syntax example Slide 47 Subject Predicate Object
  • 48. DATASUPPORTOPEN RDF Syntax Turtle Subject Predicate Object Slide 48 @prefix dcat: <http://www.w3.org/TR/vocab-dcat/> . @prefix dct: <http://purl.org/dc/terms/. < http://publications.europa.eu/resource/authority/file-type/> a <dcat:Dataset> ; dct:title “File types Name Authority List“; dct:publisher <http://open-data.europa.eu/en/data/publisher/publ> . <http://open-data.europa.eu/en/data/publisher/publ> a <dct:Agent> ; dct:title “Publications Office” . Graph See also: http://www.w3.org/2009/12/rdf-ws/papers/ws11 Definition of prefixes Description of data – triples
  • 49. DATASUPPORTOPEN RDF Syntax RDFa Subject Predicate Object Slide 49 <html> <head> ... </head> <body> ... <div resource=“http://publications.europa.eu/resource/authority/file-type/” typeof= “http://www.w3.org/ns/dcat#Dataset”> <p> <span property=" http://purl.org/dc/terms/title ">File types Name Authority List<span> Publisher: <span property="http://purl.org/dc/terms/Agent"> Publications Office</span> </p></div> </body> See also: http://www.w3.org/TR/2012/NOTE-rdfa-primer-20120607/ embedding RDF data in HTML
  • 50. DATASUPPORTOPEN How to represent data in RDF Classes, properties and vocabularies Slide 50
  • 51. DATASUPPORTOPEN RDF Vocabulary “A vocabulary is a data model comprising classes, properties and relationships which can be used for describing your data and metadata.” • Class. A construct that represents things in the real and/or information world, e.g. a person, an organisation, a concept such as “health” or “freedom”. • Property. A characteristic of a class in a particular dimension such as the legal name of an organisation or the date and time that an observation was made. In RDF properties are encoded as data type properties. • Relationship. A link between two classes; for example the link between a document and the organisation that published it (i.e. organisation publishes document), or the link between a map and the geographic region it depicts (i.e. map depicts geographic region). In RDF relationships are encoded as object type properties. Slide 51
  • 52. DATASUPPORTOPEN Examples of classes, relationships and properties: The Core Person Vocabulary in UML 52 class Healthcare Domain Core Vocabularies::Identifier dateOfIssue :dateTime [0..1] identifier :string [1..1] identifierType :string [0..1] issuingAuthority :string [0..1] issuingAuthorityUri :URI [0..1] Core Vocabularies::Person alternativeName :string birthName :string dateOfBirth :dateTime dateOfDeath :dateTime familyName :string fullName :string gender :code givenName :string patronymicName :string Core Vocabularies::Location geographicIdentifier :URI geographicName :string Core Vocabularies::Address addressArea :string addressID :string adminUnitL1 :string adminUnitL2 :string fullAddress :string locatorDesignator :string locatorName :string poBox :string postCode :string postName :string thoroughfare :string Core Vocabularies::Geometry lat :string long :string wkt :string xmlGeometry :XML address identifies geometry placeOfDeath countryOfDeath placeOfBirth countryOfBirth identifier UML: The Unified Modelling Language class diagrams provide the means for expressing the conceptual data model of vocabularies, such as the ISA Core Vocabularies, thus facilitating the understanding of the meaning of the data model. Relationships Class Properties Class Class Class Class
  • 53. DATASUPPORTOPEN Introduction to SPARQL The RDF Query Language Slide 53
  • 54. DATASUPPORTOPEN About SPARQL SPARQL is the standard language to query graph data represented as RDF triples. • SPARQL Protocol and RDF Query Language • One of the three core standards of the Semantic Web, along with RDF and OWL. • Became a W3C standard January 2008. • SPARQL 1.1 is a W3C Recommendation since March 2013. Slide 54
  • 55. DATASUPPORTOPEN Types of SPARQL queries • SELECT. Return a table of all X, Y, etc. satisfying the following conditions ... • CONSTRUCT. Find all X, Y, etc. satisfying the following conditions ... and substitute them into the following template in order to generate (possibly new) RDF statements, creating a new graph. • DESCRIBE. Find all statements in the dataset that provide information about the following resource(s) ... (identified by name or description) • INSERT. Add triples to the RDF graph. • DELETE. Delete triples from the RDF graph. • ASK. Are there any X, Y, etc. satisfying the following conditions ... Slide 55 See also: http://www.euclid-project.eu/modules/chapter2 https://joinup.ec.europa.eu/community/ods/document/tm13-introduction-rdf-sparql-en
  • 56. DATASUPPORTOPEN PREFIX dct: <http://purl.org/dc/terms/> PREFIX dcat: <http://www.w3.org/TR/vocab-dcat/> SELECT ?title WHERE { ?dataset rdf:type dcat:Dataset . ?dataset rdf:title ?title } Structure of a SPARQL Query Slide 56 Type of query Variables, i.e. what to search for RDF triple patterns, i.e. the conditions that have to be met Definition of prefixes
  • 57. DATASUPPORTOPEN SELECT – return the name of a dataset with particular URI Slide 57 <http://.../authority/file-type/> rdf:type dcat:Dataset. <http://.../authority/file-type/> dct:title “File types Name Authority List“ . <http://.../authority/file-type/> dct:publisher < http://open-data.europa.eu/en/data/publisher/publ>. < http://.../publisher/publ> rdf:type dct:Agent . < http://.../publisher/publ> dct:title “Publications Office” . PREFIX dcat: <http://www.w3.org/TR/vocab-dcat/> PREFIX dct: <http://purl.org/dc/terms/> SELECT ?dataset WHERE { <http://.../authority/file-type/> dct:title ?dataset . } dataset “File types Name Authority List” Sample data Query Result
  • 58. DATASUPPORTOPEN SELECT - return the name and publisher of a dataset Slide 58 PREFIX dcat: <http://www.w3.org/TR/vocab-dcat/> PREFIX dct: <http://purl.org/dc/terms/> SELECT ?dataset ?publisher WHERE {http://.../authority/file-type/ dct:publisher ?publisherURI. http://.../authority/file-type/ dct:title ?dataset. ?publisherURI dct:title ?publisher . } dataset publisher “File types Name Authority List” “Publications Office” <http://.../authority/file-type/> rdf:type dcat:Dataset. <http://.../authority/file-type/> dct:title “File types Name Authority List“ . <http://.../authority/file-type/> dct:publisher < http://open-data.europa.eu/en/data/publisher/publ>. < http://.../publisher/publ> rdf:type dct:Agent . < http://.../publisher/publ> dct:title “Publications Office” . Sample data Query Result
  • 59. DATASUPPORTOPEN SPARQL Example – EU ODP (1) Slide 59
  • 60. DATASUPPORTOPEN SPARQL Example – EU ODP (2) Slide 60
  • 61. DATASUPPORTOPEN SPARQL Example – EU ODP (2) Slide 61
  • 62. DATASUPPORTOPEN Summary • RDF is a general way to express data intended for publishing on the Web. • RDF data is expressed in triples: subject, predicate, object. • Different syntaxes exist for expressing data in RDF. • SPARQL is a standardised language to query graph data expressed as RDF. • SPARQL can be used to query and update RDF data. Slide 62
  • 63. DATASUPPORTOPEN Slide 63 Learning Module 3: Workshop for Publishing Open Linked EU Data
  • 64. DATASUPPORTOPEN Workshop for publishing open linked EU data This module is about... • Creating an RDF vocabulary for modelling your data.  How to reuse existing vocabularies to model your data.  How to create new classes and properties in RDF.  How and where to publish your RDF vocabulary so that it can be reused by others. • An example of how tabular data can be published as Linked Open Data using Open Refine. Slide 64
  • 65. DATASUPPORTOPEN Learning objectives By the end of this training module you should have an understanding of: • What the best practices are for creating an RDF vocabulary for modelling your data. • Where to find RDF vocabularies for reuse. • How you can create your own RDF vocabulary. • How to publish your RDF vocabulary. • The process and methodology for developing semantic agreements developed by the ISA Programme of the European Commission. Slide 65
  • 66. DATASUPPORTOPEN Creating an RDF vocabulary How to reuse other vocabularies, define your own terms, publish and promote your vocabulary Slide 66
  • 67. DATASUPPORTOPEN 6 steps for creating an RDF vocabulary Start with a robust Domain Model developed following a structured process and methodology. Research existing terms and their usage and maximise reuse of those terms. Where new terms can be seen as specialisations of existing terms, create sub class and sub properties. Where new terms are required, create them following commonly agreed best practice. Publish within a highly stable environment designed to be persistent. Publicise the RDF vocabulary by registering it with relevant services. Slide 67 1 See also: https://joinup.ec.europa.eu/community/semic/document/cookbook- translating-data-models-rdf-schemas 2 3 4 5 6
  • 68. DATASUPPORTOPEN Start with a robust Domain Model Slide 68 1 has Ceiling has Political category Amount Currency Figure Type Year Nomenclature Type Heading EU Programme Code Type Political category Code Description Corporate body Code Type Location Introduction Remark Conditions Acronym Legal base period Legal base type Legal base status has Corporate body has Nomenclature has EU Programme
  • 69. DATASUPPORTOPEN General purpose vocabularies: DCMI, RDFS To name things: rdfs:label, foaf:name, skos:prefLabel To describe people: FOAF, vCard, Core Person Vocabulary To describe projects: DOAP, ADMS.SW To describe interoperability assets: ADMS To describe registered organisations: Registered Organisation Vocabulary To describe addresses: vCard, Core Location Vocabulary To describe public services: Core Public Service Vocabulary To describe datasets: DCAT, DCAT Application Profile, VoID Reuse existing terms and vocabularies Slide 69 2
  • 70. DATASUPPORTOPEN Well-known vocabularies: Slide 70 DCAT-AP Vocabulary for describing datasets in Europe Core Person Vocabulary Vocabulary to describe the fundamental characteristics of a person, e.g. the name, the gender, the date of birth... DOAP Vocabulary for describing projects ADMS Vocabulary for describing interoperability assets. Dublin Core Defines general metadata attributes Registered Organisation Vocabulary Vocabulary for describing organizations, typically in a national or regional register Organization Ontology for describing the structure of organizations Core Location Vocabulary Vocabulary capturing the fundamental characteristics of a location. Core Public Service Vocabulary Vocabulary capturing the fundamental characteristics of a service offered by public administration schema.org Agreed vocabularies for publishing structured data on the Web elaborated by Google, Yahoo and Microsoft See also: http://www.w3.org/wiki/TaskForces/CommunityProj ects/LinkingOpenData/CommonVocabulariesReuse existing terms and vocabularies 2
  • 71. DATASUPPORTOPEN • Reuse greatly aids interoperability of your data  Use of dcterms:created, for example, the value for which should be a data typed date such as 2013-02-21^^xsd:date, is immediately processable by many machines. If your schema encourages data publishers to use a different term and date format, such as ex:date "21 February 2013" – data published using your schema will require further processing to make it the same as everyone else's. • Reuse adds credibility to your schema.  It shows it has been published with care and professionalism, again, this promotes its reuse. • Reuse is easier and cheaper.  Reusing classes and properties from well defined and properly hosted vocabularies avoids your having to replicate that effort. Slide 71 Advantages of reuse: Reuse existing terms and vocabularies2
  • 72. DATASUPPORTOPEN You can find reusable RDF vocabularies on: Slide 72 http://joinup.ec.europa.eu/ http://lov.okfn.org/ Reuse existing terms and vocabularies2
  • 73. DATASUPPORTOPEN Creation of sub-classes and sub-properties • RDF schemas and vocabularies often include terms that are very generic. • By creating sub-class and sub-property relationships, systems that understand the super property or super class may be able to interpret the data even if the more specific terms are unknown. • Do not create sub-classes and sub-properties simply to allow you to use your own term for something that already exists. Slide 73 3
  • 74. DATASUPPORTOPEN Creation of sub-classes and sub-properties Slide 74 3 The EU Budget vocabulary defines the introduction property as a sub- property of dct:description. Nomenclature Type Heading Introduction Remark Conditions
  • 75. DATASUPPORTOPEN Creation of sub-classes and sub-properties Slide 75 3 The EU Budget vocabulary defines the has nomenclature property as a sub-property of dct:subject. Amount Currency Figure Type Year Nomenclature Type Heading Introduction Remark Conditions has Nomenclature
  • 76. DATASUPPORTOPEN Where new terms are required, create them following commonly agreed best practices  Classes begin with a capital letter and are always singular, e.g. skos:Concept.  Properties begin with a lower case letter, e.g. rdfs:label.  Object properties should be verbs, e.g. org:hasSite.  Data type properties should be nouns, e.g. dcterms:description.  Use camel case if a term has more than one word, e.g. foaf:isPrimaryTopicOf. Slide 76 4
  • 77. DATASUPPORTOPEN Where new terms are required, create them following commonly agreed best practices If there is no suitable authoritative reusable vocabulary for describing your data, use conventions for describing your own vocabulary: - RDF Schema (RDFS) - Web Ontology Language (OWL) Example: defining the “Amount” class Slide 77 4 See also: http://www.slideshare.net/OpenDataSupport/model-your- data-metadata Amount Currency Figure Type Year
  • 78. DATASUPPORTOPEN Where new terms are required, create them following commonly agreed best practices If there is no suitable authoritative reusable vocabulary for describing your data, use conventions for describing your own vocabulary: - RDF Schema (RDFS) - Web Ontology Language (OWL) Example: defining the “amount type” property Slide 78 4 See also: http://www.slideshare.net/OpenDataSupport/model-your- data-metadata Amount Currency Figure Type Year
  • 79. DATASUPPORTOPEN Where new terms are required, create them following commonly agreed best practices When defining new properties, consider to define their domain and range.  A range states that the values of a property are instances of one or more classes.  A domain states on which classes a given property can be used. Slide 79 4 See also: http://www.slideshare.net/OpenDataSupport/model-your- data-metadata has Ceiling Amount Currency Figure Type Year Political category Code Description
  • 80. DATASUPPORTOPEN Publish within a highly stable environment designed to be persistent • Choose a stable namespace for your RDF vocabulary  Example: http://data.europa.eu/bud/ • Use good practices on the publication of persistent Uniform Resource Identifiers (URI) sets, both in terms of format, design rules and management.  Examples: o http://www.w3.org/ns/adms o http://purl.org/dc/elements/1.1 Slide 80 5 See also: https://joinup.ec.europa.eu/community/semic/document/cookbook-translating- data-models-rdf-schemas http://www.slideshare.net/OpenDataSupport/design-and-manage-persitent-uris
  • 81. DATASUPPORTOPEN Publicise the RDF vocabulary by registering it with relevant services Once your RDF vocabulary is published you will want people to know about it. To reach a wider audience, register it on Joinup and Linked Open Vocabularies. Slide 81 6
  • 82. DATASUPPORTOPEN Conclusions Slide 82 Start with a robust Domain Model developed following a structured process and methodology. Research existing terms and their usage and maximise reuse of those terms. Where new terms can be seen as specialisations of existing terms, create sub class and sub properties as appropriate. Where new terms are required, create them following commonly agreed best practice in terms of naming conventions etc Publish within a highly stable environment designed to be persistent. Publicise the RDF vocabulary by registering it with relevant services. Analyse Model Publish
  • 83. DATASUPPORTOPEN Example Using Open Refine for RDF to publish tabular data as Linked Data. Slide 83
  • 84. DATASUPPORTOPEN What is Open Refine Slide 84 “OpenRefine is a powerful tool for working with messy data, cleaning it, transforming it from one format into another, ...” - openRefine.org See also: Open Refine website http://openrefine.org/
  • 85. DATASUPPORTOPEN What is Open Refine RDF extension Open Refine RDF extension allows you to easily import data in different formats such as :  CSV;  Excel(.xls and .xlsx);  JSON;  XML; and  RDF/XML. And then determine the intended structure of an RDF dataset, by drawing a template graph. Slide 85 See also: LOD 2 Webinar – Open Refine http://www.youtube.com/watch?v=4Ve93C238gI
  • 86. DATASUPPORTOPEN Using Open Refine to model and publish open data Getting started 1. Install Open Refine from: https://github.com/OpenRefine 2. Install the RDF extension : http://refine.deri.ie/ And then... Describe your data in a spreadsheet. Create a project and upload it in Open Refine. Clean up the data Map your data to appropriate RDF classes & properties. Export the data in RDF. Slide 86 1 2 3 4 5
  • 87. DATASUPPORTOPEN Example situation Publish statistical data as RDF according to RDF Data Cube Vocabulary Digital Agenda Scoreboard Slide 87
  • 88. DATASUPPORTOPEN Describe your data in a spreadsheet Download the tabular data Slide 88 1
  • 89. DATASUPPORTOPEN Create a project and upload it in Open Refine Slide 89 2 Upload the spreadsheet Select relevant tabs Create the project
  • 90. DATASUPPORTOPEN Clean up the data – table harmonisation Slide 90 3 • Star & remove unnessary rows • Rename columns • Use facets to select the data to be published
  • 91. DATASUPPORTOPEN Clean up the data – prepare RDF Slide 91 3 • Create URI representation for the involved object values • via formula • via reconsiliation
  • 92. DATASUPPORTOPEN Map your data to appropriate RDF classes & properties (model your data) Slide 92 4 Understand the target vocabulary: e.g. W3C RDF Data Cube Vocabulary
  • 93. DATASUPPORTOPEN Map your data to appropriate RDF classes & properties (model your data) Slide 93 4 Define a skeleton to transform your spreadsheet data to RDF
  • 94. DATASUPPORTOPEN Map your data to appropriate RDF classes & properties (model your data) You can map the data to the ontology using a simple graphical interface to create or edit an existing RDF skeleton. You can set the base URI for the data. Slide 94 Graphical interface to copy/paste an existing RDF skeleton Graphical interface to edit an RDF skeleton 4
  • 95. DATASUPPORTOPEN Export your data to RDF/XML or Turtle Slide 95 5 Export of the data in Turtle
  • 96. DATASUPPORTOPEN Production pipelines From desk to automated pipeline Slide 96 flexibility volume OpenRefine UnifiedViews Cellar
  • 97. DATASUPPORTOPEN Thank you for your attention! ...and now YOUR questions? Slide 97
  • 98. DATASUPPORTOPEN References • 5 ★ Open Data. http://5stardata.info/ • ADMS Brochure. ISA Programme. https://joinup.ec.europa.eu/elibrary/document/adms-brochure • An organization ontology. W3C. http://www.w3.org/TR/vocab- org/ W3C. • Case study on how Linked Data is transforming eGovernment. ISA Programme. https://joinup.ec.europa.eu/community/semic/document/case- study-how-linked-data-transforming-egovernment • Common Vocabularies / Ontologies / Micromodels. W3C. http://www.w3.org/wiki/TaskForces/CommunityProjects/Linki ngOpenData/CommonVocabularies • Cookbook for translating Data Models to RDF Schemas. ISA Programme. https://joinup.ec.europa.eu/community/semic/document/cookb ook-translating-data-models-rdf-schemas • D7.1.3 - Study on persistent URIs, with identification of best practices and recommendations on the topic for the MSs and the EC. ISA Programme. https://joinup.ec.europa.eu/sites/default/files/D7.1.3%20- %20Study%20on%20persistent%20URIs.pdf • EUCLID. Course 1: Introduction and Application Scenarios. http://www.euclid-project.eu/modules/course1 • Linked Data. Tim Berners-Lee. http://www.w3.org/DesignIssues/LinkedData.html Slide 98 • Linked Data Cookbook. W3C. http://www.w3.org/2011/gld/wiki/Linked_Data_Cookbook • Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/ • Module 2: Querying Linked Data. EUCLID. http://www.euclid- project.eu/modules/course2 • Open Data – An Introduction. The Open Knowledge Foundation. http://okfn.org/opendata/ • Open Refine: https://github.com/OpenRefine • RDF Extension: http://refine.deri.ie/ • Resource Description Framework. W3C. http://www.w3.org/RDF/ • Semantic Web Stack. W3C. http://www.w3.org/DesignIssues/diagrams/sweb- stack/2006a.png • SPARQL Query Language for RDF. W3C. http://www.w3.org/TR/rdf-sparql-query/
  • 99. DATASUPPORTOPEN Further reading Slide 99 EC ISA, Process and methodology for developing semantic agreements, https://joinup.ec.europa.eu/community/core_vocabularies/documen t/process-and-methodology-developing-semantic-agreements EC ISA, Cookbook for translating Data Models to RDF Schemas https://joinup.ec.europa.eu/community/semic/document/cookbook- translating-data-models-rdf-schemas
  • 100. DATASUPPORTOPEN Further reading EUCLID - Course 1: Introduction and Application Scenarios http://www.euclid-project.eu/modules/course1 EUCLID - Course 2: Querying Linked Data http://www.euclid-project.eu/modules/course2 Learning SPARQL. Bob DuCharme. http://www.learningsparql.com/ Linked Data Cookbook, W3C Government Linked Data Working Group http://www.w3.org/2011/gld/wiki/Linked_Data_Cookbook Slide 100
  • 101. DATASUPPORTOPEN Further reading Linked Data: Evolving the Web into a Global Data Space. Tom Heath and Christian Bizer. http://linkeddatabook.com/editions/1.0/ Linked Open Data: The Essentials. Florian Bauer, Martin Kaltenböck. http://www.semantic-web.at/LOD-TheEssentials.pdf Linked Open Government Data. Li Ding Qualcomm, Vassilios Peristeras and Michael Hausenblas. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6237454 Semantic Web for the working ontologist. Dean Allemang, Jim Hendler. http://workingontologist.org/ Slide 101
  • 102. DATASUPPORTOPEN Be part of our team... Slide 102 Find us on Contact us Join us on Follow us Open Data Support http://www.slideshare.net/OpenDataSupport http://www.opendatasupport.euOpen Data Support http://goo.gl/y9ZZI @OpenDataSupport contact@opendatasupport.eu
  • 103. DATASUPPORTOPEN This presentation has been created by PwC Authors: Michiel De Keyzer, Nikolaos Loutas, Jana Makedonska, Brecht Wyns Presentation metadata Slide 103 Open Data Support is funded by the European Commission under SMART 2012/0107 ‘Lot 2: Provision of services for the Publication, Access and Reuse of Open Public Data across the European Union, through existing open data portals’(Contract No. 30-CE- 0530965/00-17). © 2015 European Commission Disclaimers 1. The views expressed in this presentation are purely those of the authors and may not, in any circumstances, be interpreted as stating an official position of the European Commission. The European Commission does not guarantee the accuracy of the information included in this presentation, nor does it accept any responsibility for any use thereof. Reference herein to any specific products, specifications, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favouring by the European Commission. All care has been taken by the author to ensure that s/he has obtained, where necessary, permission to use any parts of manuscripts including illustrations, maps, and graphs, on which intellectual property rights already exist from the titular holder(s) of such rights or from her/his or their legal representative. 2. This presentation has been carefully compiled by PwC, but no representation is made or warranty given (either express or implied) as to the completeness or accuracy of the information it contains. PwC is not liable for the information in this presentation or any decision or consequence based on the use of it.. PwC will not be liable for any damages arising from the use of the information contained in this presentation. The information contained in this presentation is of a general nature and is solely for guidance on matters of general interest. This presentation is not a substitute for professional advice on any particular matter. No reader should act on the basis of any matter contained in this publication without considering appropriate professional advice.