linked data generation process

LD4SC Summer School 7th -‐ 12th June, Cercedilla, Spain


1st Summer School on Smart Ci2es and Linked Open Data (LD4SC-‐15)

Linked Data Genera=on Process Raúl García-‐Castro, Filip Radulovic, Oscar Corcho, María Poveda, Víctor Rodríguez-‐Doncel, Asunción Gómez-‐Pérez, Daniel Vila-‐Suero

Presenter: Raúl García-‐Castro


Index

•  Linked Open Data in Smart Ci2es •  Guidelines for the Genera=on of Linked Data •  Discussion •  Hands-‐on Descrip=on

2


Data in smart ci=es

hQp://br.fiberhomegroup.com/pt/Enterprise/324/2282.aspx

3


•  For example, (re)using open transport data –  Provide travel informa=on to persons – Allow beQer mul=modal route planning –  Facilitate public transport management – … – Accessibility

•  Which metro accesses are accessible for wheelchair users? •  In which bus stops is it safer and more convenient for a wheelchair user to wait?

•  Is there any accessible parking space nearby a bus stop? •  etc.

Open data… for what?

4


Legal framework and open data ini=a=ves

•  Aarhus Conven=on (1998) –  Right to par=cipa=on and access; 41 countries and the EU

•  Open Access Ini=a=ve (2001) –  Scien=fic informa=on on the Web; > 510 organisa=ons

•  PSI Direc=ve –  PSI Reuse (2003/98/EC)

•  Conven=on for the access to official documents (2009) –  Signed by 12 countries –  Belgium, Finland, Norway, Sweden, Hungary, Estonia, Lithuania, Slovenia, Georgia,

Montenegro, Serbia and Macedonia

•  Law 37/2007. PSI Reuse •  Law 11/2007. Ci=zen access to public services and right to the quality of services •  RD 4/2010 Na=onal Interoperability Scheme

–  Open standards –  Technology neutral –  Open source solware

•  RD 1495/2011 It develops law 37/2007 •  Norma Técnica de Interoperabilidad (19/02/2013, BOE 4/3/2013)

Adapted from Antonio Rodríguez Pascual (IGN) 5


The problem: lack of interoperability

Publish

Extract

Publish

Extract

Publish

Extract

I want to publish data in an interoperable

structure and format

I use GTFS I use my own CSV structure

I provide a web service

Build an app that is available all over the

world

6


Scenario: open transport data

Is there any open transport

data already?

We are surrounded by them

7


Open data and how they are published

1) In no2ce boards –  For those who have a lot of free =me –  Or those who are there at the right moment in =me

Adapted from Antonio Rodríguez Pascual (IGN)

DATA

8



2) In web pages and mobile apps –  For people


On the Web, open license

DATA

9



2) In web pages and mobile apps –  For people



DATA

Machine-‐readable

Non-‐proprietary format



3) As web files –  So that they can be loaded by humans in their

informa=on systems (XML, HTML, CSV, etc.) –  Hopefully it is not a scanned PDF



DATA

Machine-‐readable


11

LD4SC Summer School 7th -‐ 12th June, Cercedilla, Spain Adapted from Antonio Rodríguez Pascual (IGN)


4) Via web services –  For humans and machines –  It allows genera=ng added-‐value services –  And can be integrated in the applica=on business logic


DATA

Machine-‐readable


12


What is open data?

•  Open data are data that can be freely used, reused and redistributed by anyone -‐ subject only, at most, to the requirement to a9ribute and sharealike.

•  The most important aspects to consider: –  Availability and Access: data must be available as a whole and at no

more than a reasonable reproduc2on cost, preferably by downloading over the Internet. Data must also be available in a convenient and modifiable form.

–  Reuse and Redistribu2on: data must be provided under terms that permit reuse and redistribu2on including the intermixing with other datasets.

–  Universal Par2cipa2on: everyone must be able to use, reuse and redistribute -‐ there should be no discrimina2on against fields of endeavour or against persons or groups. For example, ‘non-‐commercial’ or ‘only in educa=on’ restric=ons.

Source: Open Data Handbook 13


Scenario: open transport data

Is there any open transport

data already?

Can we do it beSer?

14


Going into 4 and 5 Linked Data

Make it available as structured data (e.g., Excel instead of image scan or a table)

Use non-‐proprietary formats (e.g., CSV instead of Excel)

Use URIs to iden2fy things, so that people can point at your stuff

Link your data to other data to provide context

Make your stuff available on the Web (whatever format) under an open license

15


USE URIs + RDF RDF standards

José

Mobility impairment

Boardgames

API

Mirasierra

Ven=squero de la Condesa

Yes

CSV

Mega Games


Yes

CSV

Mega Games

Conquer & Smash!

MG

29,95

HTML

José

Mobility Impairment

hasImpairment

WheelchairAccessibility

requires

Boardgame

likes

Mirasierra

address Ven=squero de la Condesa


hasAccessibility

Mega Games

address

hasAccessibility WheelchairAccessibility


Mega Games

Conquer & Smash!

is a Boardgame

sells

API RDF CSV RDF CSV RDF HTML RDF


Link your data Linked RDF

José

Mobility impairment

Boardgames

Mirasierra


Yes

Mega Games


Yes

Mega Games

Conquer & Smash!

MG

29,95

API CSV CSV HTML

José

Mobility Impairment

hasImpairment


requires

Boardgame

likes

Mirasierra



Mega Games

address


Mega Games

Conquer & Smash!

is a

hasAccessibility

Boardgame


sells





Boardgame

Link your data Linked RDF

José

Mobility impairment

Boardgames

Mirasierra


Yes

Mega Games


Yes

Mega Games

Conquer & Smash!

MG

29,95

API CSV CSV HTML

José

Mobility Impairment

hasImpairment


requires

Boardgame

likes

Mirasierra



Mega Games



Mega Games

sells Conquer & Smash!

is a Boardgame



Make complex queries

Where can I buy the Conquer & Smash!

game?

Which are the most accessible routes for Christmas shopping?

Expansion pack for Conquer & Smash! Take metro line 9 and in 35 minutes

we can demo it to you!

Or beQer take bus 231 because it is sunny and you can take a glance at the outdoor art

exhibi=on in Plaza de Cas=lla

MG


Using Linked Open Transport Data

•  Calculate accessible routes –  Combined with geographical data (IGN) – Which stop should I use if I have mobility problems?

•  Commercial routes by bus –  Combined with Madrid’s shop census (from Ayto. Madrid)

•  Geomarke=ng decisions for enterpreneurs – Where should I open my shop? Based on the combina=on of the number of travellers per stop, demographic data, data about other businesses and shops around, etc.

•  Personalised offers to travellers – With real-‐=me data and data about consump=on paQerns (e.g., credit card transac=ons)

•  …

20


Index

•  Linked Open Data in Smart Ci=es •  Guidelines for the Genera2on of Linked Data •  Discussion •  Hands-‐on Descrip=on

21


Linked Data life cycle

Specification

Modelling

Generation Publication

Exploitation

Linking

22


Requirements (smart ci=es domain)

1.  Tabular formats (i.e., SQL, XLS or CSV) –  Other data structures (e.g., XML) less important in prac=ce

or are unstructured and would require much more work 2.  Changing data (dynamic or streaming data), versioning,

(automa=c) data quality assurance and reliability 3.  Data access through web services, proprietary APIs and

data files 4.  Legal aspects (e.g., licensing, data ownership) 5.  Access rights management or mechanisms for

extrac=ng public data (plenty of confiden=al data)

23


Linked Data genera=on process

Select data source

Obtain access to

data source

Analyse data source

Analyse licensing of

the data source

Define resource naming strategy

Transform data source

Link with other

datasets

Data source

Access, data

License

Schema, data

Resource naming strategy

Ontology

RDF data

Linked dataset

Ontology Develop ontology

24

F. Radulovic, M. Poveda-‐Villalón, D. Vila-‐Suero, V. Rodríguez-‐Doncel, R. García-‐Castro and A. Gómez-‐ Pérez, Guidelines for Linked Data genera=on and publica=on: An example in building energy consump=on, Automa=on in Construc=on, Special Issue on Linked Data in Architecture and Construc=on. Available online April 2015.



Select data source

Obtain access to

data source

Analyse data source


the data source



Link with other

datasets

Data source

Access, data

License

Schema, data


Ontology

RDF data

Linked dataset


DATA PREPARATION

25


Select data source

•  Select the data source that will be transformed into Linked Data

•  Steps: – To define the requirements for selec=on – To select one or several data sources

•  The data set may be: – Owned by your organiza=on… – … or not (external data sources)

26


Select data source – LCmple

•  Requirements –  Real-‐world scenario in the smart city domain – Available for use – Available in machine-‐processable format (the more structured the data are, the beQer)

–  Can be linked with generic en==es (e.g., loca=on) •  Leeds City Council – energy consump=on

–  hQp://data.gov.uk/dataset/council-‐energy-‐consump=on

27


Obtain access to data source

•  Data access means –  Technical means to retrieve the data –  Legal rights to use the data

•  If the data is not accessible: –  To iden=fy the person to contact –  To request the access –  To obtain access and to retrieve the data

•  Access alterna=ves: –  file, –  programming interface, –  database, –  data stream, –  etc.

28


Obtain access to data source – Lample

•  Data set already available as a CSV file

29


Analysing licensing of the data source

•  Licenses specify the legal terms under which a data set can be used and exploited

•  Neither legal prescrip=ons on how to declare licenses nor common standard prac=ces to do so

•  Steps (not automatable): –  To iden=fy the rightsholder and the authorita=ve publisher

•  Righstholder vs. authorized distributor –  To find the applicable license

•  Web page, data set metadata, data themselves •  Contact the publisher

–  To read the license and analyse legal terms •  Tips

–  Analysis should be performed upon all copies and formats of the data –  Ensure license compa=bility when integra=ng several data sources

30


Linked Data resources can be protected

Ontologies are intellectual works, they can be protected by copyright RDF Datasets can be considered as databases, also legally protected in the EU

31


Create, consume, aggregate, derive and publish Linked Data in a lawful environment

0

Always license your data

…

Data shops Government Individuals

32


Licensed Linked Data

Non-‐licensed Linked Data Licensed Linked Data

+License

Unless there is a license allowing to do so, the resource cannot be copied, modified or published. In practice, non-licensed resources are useless in industrial settings

Licensed Linked Data can be used

33


Licensed Linked Data in prac=ce

Linked Open Data Published Open License

(Published) Linked Data Published No Open License

Linked Data Not Published No Open License

34


ç

Guidelines for licensing linked data

35

Add "rights" metadata in the dataset descrip=on (e.g., VoID, DCAT) 1

Use standard predicates to declare "rights" statements (e.g., Dublin Core terms: dc:rights, dct:license) 2

?

Use rights declara2on language, e.g., ODRL

Yes

Use URI of standard license e.g., CC0 3b 3a

No

Standard license available

ODRL Open Digital Rights Language

DCAT Data catalog vocabulary


Licensing Linked Data is Simple…

The Bri=sh Na=onal Bibliography (BNB) lists the books and new journal =tles published or distributed in the United Kingdom and Ireland since 1950.

J 36


… or complex depending your needs

Policies can be expressed with ODRL 2.0 to govern access to Linked Data Example of access to Linked Data for a price (15EUR for the dataset or 0.01EUR for a triple thereof)

@prefix gr: <http://purl.org/goodrelations/> . @prefix dcat: <http://www.w3.org/ns/dcat#> . <http://salonica.dia.fi.upm.es/ldr/policy/cdaddba4-fc2e-4ee0-a784-e62f1db259bf> a odrl:Set ; rdfs:label "License Offering Paid Linked Data" ; odrl:permission [ a odrl:Permission ; odrl:target <http://example.org/dataset/ds01> ; odrl:action odrl:reproduce ; odrl:duty [ a odrl:Duty ; rdfs:label "Pay" ; gr:UnitOfMeasurement dcat:Dataset ; gr:amountOfThisGood "1" ; odrl:action odrl:pay ; odrl:target "15,00 EUR" ] ] , [ a odrl:Permission ; odrl:action odrl:reproduce ; odrl:target <http://example.org/dataset/ds01> ; odrl:duty [ a odrl:Duty ; rdfs:label "Pay" ; gr:UnitOfMeasurement rdf:Statement ; gr:amountOfThisGood "1" ; odrl:action odrl:pay ; odrl:target "0,01 EUR" ] ] ..

The target can be an ontology, a dataset, a SPARQL endpoint… …or a SPARQL query itself or a triple pattern: {mysubject, ?p , ?o}

37


And you have support for that

•  Condi=onal access to Linked Data –  hQp://condi=onal.linkeddata.es

•  Dataset of licenses in RDF –  hQp://rdflicense.appspot.com

•  ODRL Profile for Linked Data –  hQp://purl.oclc.org/NET/ldr/ns# –  hQps://www.w3.org/community/odrl/profile/linkeddata/

38


Analyse licensing – LCmple

39


Analyse data source

•  Get insight into the data structure and organiza=on •  Steps:

–  To analyse the characteris=cs of the data •  Data values, data ranges, etc.

–  To obtain the schema of the data •  Concepts and their rela=onships

•  Data can be available as: –  Structured data –  Unstructured data

•  If the schema does not exist: –  Use a standard modeling language for describing the data schema (e.g., UML)

40


Analyse data source – LCmple

•  Metadata not quite descrip=ve: – Different types of council sites (mostly buildings)

–  Electricity, gas and oil consump=ons

–  1-‐year intervals -‐ 2010/11, 2011/12, 2012/13

•  Analysis required contac=ng with people from LCC open data

41



42

hQp://localhost:3333/



43



•  Analyse the characteris=cs of data using facets •  Obtain the schema of the data

44


Data characteris=cs and schema – LCCLLIDD

Column Type Comments / Range (rounded) Problems

uprn String Not unique, empty values

Site Name String Unique? Site types + name

4 repeated sites

Address 2 String Not unique, empty values

Address 3 String Not unique, empty values Village? Civil Parish?

Address 4 String Not unique, empty values City? Metropolitan district? “leeds” vs “Leeds”

PostCode String Not unique, empty values

Electricity 10/11 Decimal 0 — 2.700.000



Gas 10/11 Decimal -‐100,000 — 6,100,000 Nega=ve values



Oil 12/13 Decimal -‐1,000,000 — 13,000,000 Nega=ve values 45



Select data source

Obtain access to

data source

Analyse data source


the data source



Link with other

datasets

Data source

Access, data

License

Schema, data


Ontology

RDF data

Linked dataset


DEFINE RESOURCE NAMING STRATEGY

46


Hash and slash URIs

•  Hash URIs (#) –  hQp://www.energycompany.com/about#energyCompany –  The fragment part has to be stripped off when the URI is requested from the server (i.e., the resource cannot be retrieved directly)

–  Hash URIs can be used to iden=fy non-‐document resources •  Slash URIs (/)

–  hQp://www.energycompany.com/about/energyCompany –  Imply a 303 redirec=on to the loca=on of a document that represents the resource (+ content nego=a=on)

•  E.g., hQp://www.energycompany.com/about/energyCompany.rdf –  Drawbacks: HTTP round-‐trip, redirects, web server configura=on

47


Hash or slash?

•  Depends on the data and on their expected use •  Small data:

–  Hash namespace –  Access all the data as a whole –  HTTP GET would return a single informa=on resource with everything

•  Large / frequently-‐updated / modular data: –  Slash namespace –  Access resources individually or in groups –  Resource descrip=ons may be divided among many informa=on resources or may be managed via a query service (e.g., SPARQL)

–  Progressively greater detail about resources may be retrieved through mul=ple accesses

48



•  Steps: –  To choose a URI form (hash or slash) –  To choose a domain for the URIs. –  To choose a path for the URIs. –  To choose a paQern for ontology classes and proper=es in the ontology, as well as for individuals

•  Tips: –  One URI must iden=fy only one item (e.g., avoid mixing with web pages and real-‐world objects)

–  URIs should be persistent and should not change over =me (e.g., state informa=on); PURL may support this

–  Use a domain that is under your control (or a service such as PURL)

–  Separate the ontology model from its instances –  Define meaningful URIs

49


Resource naming strategy – LCC

•  Hash URIs for ontological terms, slash URIs for individuals •  Domain: hQp://smartcity.linkeddata.es/ •  Ontological terms path:

–  hQp://smartcity.linkeddata.es/lcc/ontology/EnergyConsump=on#

•  Individuals path: –  hQp://smartcity.linkeddata.es/lcc/resource/

•  Ontological terms paSern: –  hQp://smartcity.linkeddata.es/lcc/ontology/EnergyConsump=on#<term_name> –  Ex.: hQp://smartcity.linkeddata.es/lcc/ontology/EnergyConsump=on#hasQuan=ta=veValue

•  Individuals paSern: –  hQp://smartcity.linkeddata.es/lcc/resource/<resource_type>/<resource_name> –  Ex.: hQp://smartcity.linkeddata.es/lcc/resource/LeisureCentre/WetJohnCharlesCentreforSport

50



Select data source

Obtain access to

data source

Analyse data source


the data source



Link with other

datasets

Data source

Access, data

License

Schema, data


Ontology

RDF data

Linked dataset


DEVELOP ONTOLOGY

51


Ontology development

6. Ontologyimplementation

5. Ontology selection

1. Requirements definition

Can you represent all your data?

7. Ontology evaluation

2. Terms extraction

3. Ontology conceptualization

4. Ontology search

6.2 Ontology completion

3.1 Initial model drafting

3.2 Detailed model definition

6.1 Ontology integration

You did this yesterday

52


Ontology development – LCCDD

53



Select data source

Obtain access to

data source

Analyse data source


the data source



Link with other

datasets

Data source

Access, data

License

Schema, data


Ontology

RDF data

Linked dataset


TRANSFORM DATA

54


Data transforma=on

•  Steps: –  To select the RDF serializa=on

•  RDF/XML, Turtle, N-‐Triples, JSON-‐LD –  To select a tool. Depends on:

•  The format of the data (database, spreadsheets, etc.), •  Concrete needs of the transforma=on process (e.g., dynamicity)

–  To transform the data into RDF •  Usually requires a mapping between the data and the ontology

•  The mapping implements the resource naming strategy –  To evaluate the obtained RDF data:

•  Syntax, Completeness, Accuracy, Conciseness, Modelling, Understandability, Versa=lity, Usage, Licensing, …

55


Data transforma=on tools

Database to RDF Data streams to RDF •  morph-‐RDB •  D2R Server •  TopBraid Composer

•  morph-‐streams •  D2R Server

Spreadsheets to RDF

XML to RDF

•  TopBraid Composer •  Excel2RDF •  RDF123 •  XLWrap •  OpenRefine/LODRefine

•  XML2RDF •  TopBraid Composer •  OpenRefine/LODRefine

56


Data transforma=on tools

Database to RDF Data streams to RDF •  morph-‐RDB •  D2R Server •  TopBraid Composer

•  morph-‐streams •  D2R Server

Spreadsheets to RDF

XML to RDF

•  TopBraid Composer •  Excel2RDF •  RDF123 •  XLWrap •  OpenRefine/LODRefine

•  XML2RDF •  TopBraid Composer •  OpenRefine/LODRefine

Overview of OpenRefine

57


OpenRefine basic opera=ons •  Installing •  Crea=ng a new project •  Data analysis

–  Exploring data –  Sor=ng data –  Face=ng data –  Filtering data

•  Basic data transforma=on (cleaning/preparing) –  Columns:

•  Move •  Rename •  Remove columns •  Collapse and expand •  Common transforma=ons

–  Rows: •  Remove rows

•  Export whole project

58


Adding derived columns Edit column à Add column based on this column...

59


Spli�ng data accross columns Edit column à Split into several columns...

60


Handling mul=-‐valued cells Edit Cells à Split mul=-‐valued cells...

Edit Cells à Join mul=-‐valued cells...

61


Rows and records Show as: rows records

Record

Row

62


Clustering similar cells Edit cells à Cluster and edit...

63


Transposing rows and columns Transpose à Transpose cells across columns into rows...

Transpose à Columnize by key/value columns...

64


Other useful u=li=es •  Regular expressions

–  Java regular expressions •  Custom transforma=ons

–  General Refine Expression Language (GREL) –  Jython (Python implemented in Java) –  Clojure (func=onal language that resembles Lisp)

65


66

Using the project history

•  Project history: – Access opera=on history – Undo opera=ons – Extract opera=ons (in JSON) – Apply opera=ons

•  Cau=on: – Transforma=ons are registered in the history; filters and facets are not


Solving memory problems

hQps://github.com/OpenRefine/OpenRefine/wiki/FAQ:-‐Allocate-‐More-‐Memory

67


OpenRefine RDF extension -‐ RDF skeleton

•  Resource naming strategy – Ontological terms paQern:

hQp://smartcity.linkeddata.es/lcc/ontology/EnergyConsump=on#<term_name>

–  Individuals paQern: hQp://smartcity.linkeddata.es/lcc/resource/<resource_type>/<resource_name>

Add base URI Add prefixes

68


Crea=ng individuals

schema:CivicStructure

rdf:type

lccRes:CouncilOfficesBelgraveHouse

69


Previewing results

70


Adding property values

rdfs:label schema:CivicStructure xsd:string

rdf:type

lccRes:CouncilOfficesBelgraveHouse rdfs:label

“Belgrave House”

71


Expor=ng RDF

@prefix schema: <http://schema.org/> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix lcc: <http://smartcity.linkeddata.es/lcc/ontology/EnergyConsumption#> . @prefix owl: <http://www.w3.org/2002/07/owl#> .

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

<http://smartcity.linkeddata.es/lcc/resource/CivicStructure/CouncilOfficesBelgraveHouse> a schema:CivicStructure ; rdfs:label "Belgrave House" .

<http://smartcity.linkeddata.es/lcc/resource/CivicStructure/CommunityCentreTunstallRoad> a schema:CivicStructure ; rdfs:label "Tunstall Road" .

Export à RDF as Turtle

72


Evalua=ng the exported data

•  Manual inspec=on •  Syntax evalua=on (with syntax validator) •  Consistency with the ontologies (with reasoner) •  Usage evalua=on (e.g., by running SPARQL queries) – Show all electricity consump=ons and the related =me periods for all council sites related to culture

– Show all energy consump=ons and the related =me periods of council sites from the Wakefield district

73


Index

•  Linked Open Data in Smart Ci=es •  Guidelines for the Genera=on of Linked Data •  Discussion •  Hands-‐on Descrip=on

74


75

Richer schema (and data) time:Interval

schema:City

ssn:Observation

ssn:observationSamplingTime

ssn:SensorOutput

ssn:ObservationValue

ssn:hasValue

ssn:FeatureOfInterest

ssn:featureOfInterest

lcc:hasQuantityValue :: xsd:decimal ssn:Property

ero:FinalEnergy

ssn:observedProperty

ssn:observationResult

LegendClassdatatype property :: datatype

object property subclass of relation

schema:CivicStructurelcc:uprn :: xsd:Stringdc:title :: xsd:String

schema:PostalAddressschema:addressLocality :: xsd:Stringschema:addressRegion :: xsd:Stringschema:streetAddress :: xsd:Stringschema:postalCode :: xsd:String

schema:address

admingeo:District

admingeo:district

time:Instanttime:inXSDDateTime :: xsd:dateTime

time:hasBeginningtime:hasEnd

ero:EnergyConsumerFacility

ero:consumesEnergyType

om:Unit_of_measure

lcc:hasQuantityUnitOfMeasurement

SupplyOrStorageSite

OpenAirSite

AccomodationSite AdministrativeSite

OfficeSite

EducationalSite

SocialSite

OtherSite

CulturalSite

schema:containedIn

schema:Place

schema:AdministrativeAreaLeisureSite


Linked Data are just data

01000000

electric1011

01000000

electric1112

01000000

0 20 40 60 80 100

electric1213

Building

Electrical consumption

0e+00

2e+06

4e+06

6e+06

8e+06

0 500000 1000000 1500000 2000000Electricity

Gas

Electricity vs gas consumption 12/13

0.0e+00

4.0e+06

8.0e+06

1.2e+07

0 500000 1000000 1500000 2000000Electricity

Oil

Electricity vs oil consumption 12/13

76


77

Benefits of linking data

resPlus$electricTotal

0e+00

2e+06

4e+06

6e+06

Total electric consump2on Original data + geoloca=on

resPopulation$electricTotal

0e+00

2e+06

4e+06

6e+06

Total electric consump2on in loca2ons with popula2on > 20.000 Original data + geoloca=on + popula=on


Benefits of reasoning

resPlus$electricTotal

250000

500000

750000

1000000

Total electric consump2on in cultural buildings

schema:CivicStructure

CulturalSite

Museum Library

78


Index

•  Linked Open Data in Smart Ci=es •  Guidelines for the Genera=on of Linked Data •  Discussion •  Hands-‐on Descrip2on

79


What are we going to do?

Specification

Modelling

Generation Publication

Exploitation

Linking

80


What are we going to do?

Select data source

Obtain access to

data source

Analyse data source


the data source



Link with other

datasets

Data source

Access, data

License

Schema, data


Ontology

RDF data

Linked dataset


81


Hands-‐on task 1 •  Goal: to get familiar with the first steps in the Linked

Data genera=on process •  The students will have to take their selected dataset(s)

and perform the following tasks: –  Analyse Data Set

•  Both the data (quan==es, value ranges, etc.) and the schema –  Analyse Licensing of the Data Source

•  Who is the publisher and the rightsholder? •  What is the licence? •  Which will be the license to be used for the generated dataset?

–  Define Resource Naming Strategy •  For the ontology and the data (URI form, content nego=a=on, URIs domain, path, paQerns, etc.)

–  Finish Ontology Development •  Lightweight ontology (i.e., classes, proper=es, domains and ranges)

82


Hands-‐on task 1 -‐ Deliverables

•  A document that includes: – The analyses performed over the data source – The licensing of the data source and the poten=al license

– The resource naming strategy defined

•  An OWL file with the ontology developed, according to the resource naming strategy defined

83


Hands-‐on task 2 •  Goal: to get familiar with the transforma=on of CSV data into RDF using LODRefine

•  The students will have to take their selected dataset(s) and perform the following tasks: –  Import data into LODRefine – Analyse and fix data

•  Analysis performed in the previous class, but can be updated with new findings

•  Fix the data to remove errors •  Transform the data to facilitate RDF genera=on

–  Export data to RDF •  Define an RDF skeleton for the data •  Export the data to RDF (Turtle syntax)

84


Hands-‐on task 2 -‐ Deliverables

For each dataset: •  An RDF file in the Turtle syntax with the data transformed into RDF

85



1st Summer School on Smart Ci2es and Linked Open Data (LD4SC-‐15)

Thank you for your aQen=on!

linked data generation process

Science

spain ld4scsummerschool

spain forexample

spain scenario

spain theproblem

spain datainsmartci

onprocess ralgarcacastro

ontopersons allowbeqermul

ve psireuse200398ec