Ontology Engineering at Scale forOpen City Data Sharing
The 8th Joint International Semantic
Technology Conference (JIST2018)
Awaji City, Hyogo, Japan, 27/11/2018
With contributions from María Poveda, Raúl García-Castro
and Paola Espinoza
Oscar Corcho
ocorcho@fi.upm.es@ocorcho, @opencitydata_es
https://www.slideshare.com/ocorcho

Talk Objectives• Share some experiences in the application of Open
Data principles for cities (in Spain)• Technical challenges and opportunities• Barriers for adoption• Required organisational changes for successful Open Data
strategies• With a special focus on how ontologies (aka agreed
vocabularies and shared data structures) can make adifference• Small bias towards how we are doing it in Spain• And how we are adapting existing ontology
engineering practices to achieve this goalOntology Engineering at Scale forOpen City Data Sharing

Building Agreed Vocabularies andData Structures for Successful
Open City Data SharingBuilding Agreed Vocabularies andData Structures for Successful
Open City Data Sharing

What is Open Data?
• Open data is data that can be freely used, re-used
and redistributed by anyone - subject only, at most, tothe requirement to attribute and sharealike• Key aspects:• Availability and access: the data must be available as awhole and at no more than a reasonable reproduction cost,preferably by downloading over the Internet. The data mustalso be available in a convenient and modifiable form.
• Re-use and redistribution: the data must be provided
under terms that permit re-use and redistribution including
the intermixing with other datasets.
• Universal participation: everyone must be able to use, reuse and redistribute - there should be no discrimination
against fields of endeavour or against persons or groups[source: Open Data Handbook, http://opendatahandbook.org/en/what-is-open-data/ ]Relevant Legislation. Europe and Spain
•
•
•
•

•
•
•
•
•

Open Access Initiative (2001). Scientific information; > 510 orgs
Aarhus Convention (1998). Right to participate and access; 41
countries and the EU
PSI Directives. PSI reuse (2003/98/EC and 2013/37/UE)
Convention about access to official documentation (2009)• 12 countries

Law 37/2007. PSI reuse (transposition of directive 2003/98/EC)• Modified in law 18/2015 (BOE 10/07/2015, directive 2013/37/UE)
Law 11/2007. Citizen access to public services, and rights to goodquality services
RD 4/2010 Esquema Nacional de Interoperabilidad• Open standards, technology neutral, open source
RD 1495/2011 It develops Law 37/2007 for national agencies
Norma Técnica de Interoperabilidad (19/02/2013, BOE 4/3/2013)[source: based on material from Antonio Rodríguez Pascual (CNIG)]

An Explosion of Open Data Portals

Open Data and how to publish it
In a posterboard• For those with a lot of free time available• Or those who happen to be there at the right time

Adapted from: Antonio Rodríguez Pascual (IGN)

Open Data and how to publish it
On a Web page or mobile app (for human consumption)

Adapted from: Antonio Rodríguez Pascual (IGN)

Open Data and how to publish it
On a Web page or mobile app (for human consumption)• Generated from structured formats (e.g., GTFS)

Adapted from: Antonio Rodríguez Pascual (IGN)

Open Data and how to publish it
In files• These can be loaded in information systems(XML, HTML, CSV, GTFS, etc.)• Luckily, not scanned PDFs

Adapted from: Antonio Rodríguez Pascual (IGN)

Open Data and how to publish it
Using Web Services or REST APIs
• Useful for developers• Ease of integration in the application logic

Adapted from: Antonio Rodríguez Pascual (IGN)

All together…, Shaken, not stirred…

Open Data and how to publish it
Using semantically-enhanced and linked APIs
• Also useful for developers• Standardised formats (JSON, JSON-LD, RDF)
• Standardised models (vocabularies, ontologies)

Data representation formats
x Non reusable√ Reusable.
x Not open

√ Reusable, open.
x Incomplete√ Reusable, open,
completex Not linked√ Reusable, open,
complete, linked

And many more: JSON, JSON-LD, Marc21, Shapefiles, KMZ,
KML, PC-Axis, etc.

Building Agreed Vocabularies andData Structures for Successful
Open City Data Sharing

Why our interest in cities• Regional and national datasets are useful to provide
indicators, comparisons across countries, etc.
• However, local datasets are usually better for thegeneration of economical and/or social value• Closer to people• Closer to businesses• Closer to developers

Why our interest in cities• Node of the Open Data Institute for Madrid• Spin-off focused on providing open data support tomunicipalities• Involved in:• Leaders of the Spanish thematic network on Open Data for
Smart Cities• Leaders of the UNE178301:2015 technical norm on Open
Data for Smart Cities• Core team members of the FEMP (Spanish Federation of
Municipalities and Provinces) working group on Open Data
• Technical and innovation support for local open data
initiatives (Zaragoza, Madrid, Alcobendas, RivasVaciamadrid, Arganda del Rey)
• Also national (http://datos.gob.es/) and regional (Aragón,
Comunidad de Madrid, CRTM)

Project Ciudades Abiertas (funded by red.es)
July 2018 – June 2020

INICIATIVA PLATAFORMA DE GOBIERNO ABIERTO,COLABORATIVA E INTEROPERABLE (121/17-SP)

More details soon at http://www.ciudadesabiertas.es/

The current status of Open City Data in Spain

Source: CTIC and OKFN

Open Data Portals in Spain• Around 60 open data portals for Spanish cities (andgrowing)• Gijón, Santander, Bilbao, Azkoitia, Azpeitia, San Sebastián,Pamplona, Zaragoza, Lleida, Manresa, Barcelona,
Badalona, El Prat, Cornellá, Viladecans, Gavá (and a few
more in Barcelona’s province), Tarragona, Valladolid,
Alcobendas, Madrid, Cáceres, Valencia, Lorca, Málaga,
Arona, etc.

• Also regional open data portals• Many of which aggregate local data
• Cabildo de Tenerife, Diputación de Málaga, Gobierno de
Aragón, etc.

• And national institutions, public companies, etc.

Relevant legislation for cities• City Ordinances on Transparency, Access and Reuse
of Information• Zaragoza:
http://www.zaragoza.es/ciudadania/gobierno-abierto/
participar/detalle_Normativa?id=3983• Initial draft (25/06/2012)• Final approval (01/04/2014)• Template from the Spanish Federation of Municipalities andProvinces - FEMP (may 2014)• http://www.femp.es/files/11-5133-fichero/
Ordenanza%20Transparencia,%20Acceso%20y%20Reutilizaci%C3%B3n%20de%20la%20informaci%C3%B3n.pdf• 2015. UNE 178301. Ciudades Inteligentes. Datos
abiertos (Open Data). MINETUR Y AENOR• More about this to come later in this presentation

Technology that is used

• Ad-hoc developments over CMSs••••

Zaragoza (http://datos.zaragoza.es/)
Madrid (http://datos.madrid.es/)
Gijón (http://datos.gijon.es/)
http://datos.gob.es• CKAN• The most recent ones, in general (e.g.,
http://opendata.aragon.es, http://datos.alcobendas.org/)• Socrata• Rubí, Gavà, etc.

• More details (in Spanish) at• http://datos.gob.es/sites/default/files/informe-herramientaspublicacion-od_vfinal.pdf

Building Agreed Vocabularies andData Structures for Successful
Open City Data SharingBuilding Agreed Vocabularies andData Structures for Successful
Open City Data Sharing

Main challenges for success• Many efforts so far focused on setting up thetechnology infrastructure• That’s ok, but content is probably more important• Heterogeneity on the selection of datasets, theirformats and granularity• Build an app, deploy everywhere• Open data portals are at the end of the data
production chain• They are not an integral part of data management inside
cities•Note: the following slides are based on my Semantics 2015
dinner speech on “Slow-cooked data and APIs”

Rule 1.
Chop your onions
appropriately

Rule 1. Chop your onions appropriately• Take care about the number of datasets that you
produce• There’s still a silly competition about“my open data portal has more datasets than yours”• This provokes, sometimes, over-segmentation of data

• Main question: What makes a dataset useful andwhich datasets should I publish?

Rule 1. Chop your onions appropriately• UNE 178301:2015• Norm on Open Data for
Smart Cities• Organised by• AENOR CTN 178 group• Government and Mobility• Government• Open Data
(led by Localidata)• Formed by• Several cities• Private companies• Nation-wideorganisations

Rule 1. Chop your onions appropriately
Datasets• 11 datasets selected• Based on frequency ofrequests from reusers

Cultural Agenda
Traffic
Population• Slow but steady adoption
for 2015 and 2016

Streets
Public Transport
Touristic Places and POIs
BudgetShop Census
Air Quality
Contracts
Parkings• And now working on extending it to 100 datasets• With an additional group of people

Rule 2.
Add some spices,
but not too many

Rule 2. Add some spices, but not too many
• Annotate (semantically) your data, so that others canunderstand what you produce• And produce examples for consumers to understand them

• Don’t wait until all schema.org properties are settled• Generate SKOS thesauri for your own classifications• e.g., for groups of citizens (young, elderly, etc.), for types of
events (cultural, children, music, etc.)

Rule 3. Try different ways of plating up your foodRule 3. Try different ways of plating up your food
• Produce your data in different formats••••

Agreed-upon JSONs
JSON-LD
RDF
Agreed-upon CSVs
• CSV on the Web• But don’t get crazy at offering all options• The ones that get finally used are more than enough

Rule 4.
Let children appreciate (and cook) open data

Rule 4. Let children appreciate (and cook) open data
Let children understand thebenefits of open data
(and Citizen Science)and how they can contribute toimproving the data of their city

Rule 5….
Eat your own dog foodLet’s better say…

Rule 5.
Try it out yourself first…

… Before giving yourfood to your customers

Rule 5. Try it out yourself first…
• Open data by default• So that your applications are also based on open data

Source: Los Datos Abiertos como Eje Central del desarrollo de la Plataforma de Gobierno Abierto. M.J. Fernández-Ruiz, V. MorlánBuilding Agreed Vocabularies andData Structures for Successful
Open City Data SharingBuilding Agreed Vocabularies andData Structures for Successful
Open City Data Sharing

Whom of you has never ever eaten a burger in his/her life?
Well, if you are vegetarian, we can think of something elseRule 6.
Fast food has its value
as well, why not…

You go anywhere in the world and know how
McDonald’s burgers are…
So let’s only learn this from fast food..

Rule 6. Fast food has its value as well, why not…
• When we open our data, let’s use at least the samedata structures
I want to publish
my dataPublishPublishPublishExtract

Extract

Extract

I am using GTFS

I am using my own CSV
structure

I provide it as a Web
service

Write an app and deploy everywhereRule 6. Fast food has its value as well, why not…

UNE 178301:2015. Context• UNE 178301:2015• Norm on Open Data for
Smart Cities• Organised by• AENOR CTN 178 group• Government and Mobility• Government• Open Data
(led by Localidata)• Formed by• Several cities• Private companies• Nation-wideorganisations

UNE 178301:2015. Objectives• Establish metrics and indicators that allow
evaluating the degree of maturity of open dataelaborated or curated by the public sector…
• … so that we can facilitate reuse,• …
• … and define a minimal list of datasets that shouldbe published by all open city data initiatives, together
with their associated vocabularies

UNE 178301:2015. The team• Coordinator• Esther Minguela (Localidata)• Oscar Corcho (OEG-UPM and Localidata)• 35 members who belong to…
• Medium&Large Cities (10) – mostly City Information
Managers• Private companies working for the public sector (6)• Regions (3) – mostly Region Information Managers• Ministries or alike (3)• Geographic sector (3)• The core of the work done over a period of 6 months

UNE 178301:2015. The team•

Coordinators••

Localidata. Esther Minguela y Oscar
CorchoPArticipants
1.
2.
3.

Animsa. Iñigo Sanciñena
Animsa. Jesús Vera
Ayuntamiento de Albacete. ManuelTobarra4. Ayuntamiento de Bilbao. Teresa Alba
5. Ayuntamiento de Bilbao. Josu Santacruz
6. Ayuntamiento de Burgos. José María Diez
7. Ayuntamiento de Madrid. Enrique Crespo
8. Ayuntamiento de Madrid. José Luis Cano
9. Ayuntamiento de Rivas. Jesús García
10. Ayuntamiento de Segovia. Alberto Gómez
11. Ayuntamiento de Zaragoza. María Jesús
Fernández
12. Comunidad de Madrid. Inmaculada
Sánchez
13. Comunidad de Madrid. Concepción García
14. IGN. Antonio Rodríguez
15. IGN. Celia Sevilla
16. COIT. Jorge Díaz
17. CRTM. Concha Chapa
18. CRTM. José Antonio Cascales

19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.

Etra. Vicente Sebastián
FEMP. Pablo Bárcenas
Geoactio. Pello Gámez
Gmv. Antonio Velasco
Indra. Elisabet Terrades
Indra. Jordi Marín
Junta de Castilla y León. Antonio Ibáñez
MINETUR. Ana Pérez
MINETUR. Javier García
MINHAP. Aleida Alcaide
Proconsi. Constantino Lázaro
Red.es. José Ignacio Sánchez
Red.es. Sonia Castro
SEGITTUR. Calixto Mellen
SEGITTUR. Enrique Lancis
SEGITTUR. Rosa Muñoz
SEGITTUR. Susana García
Universidad de Zaragoza. Javier López

UNE178301:2015. Metrics
Strategy
Domains

Strategy

Legal
Legal

Organisational
Organisation
Measurements

Technical
Availability
AccessDimensions

Data Quality
UpdateSocio-economic
Reuse
Participation and Collaboration

Raw data
Completeness
Documentation
Correctness
Geo-referencing
Linked Data

UNE 178301. Metrics. Example

UNE178301:2015. High-Priority Datasets• Listing based on thecurrent inventories fromall cities (and regions)• Harmonisation• Votes according to PSIreuse requests

Datasets
Cultural Agenda
Traffic
Population
Streets
Public Transport
Touristic Places and POIs
BudgetShop Census
Air Quality
Contracts
Parkings

UNE 178301:2015. Vocabularies
DatasetVocabulary

General – sharedacross datasets

Postal address:
http://vocab.linkeddata.es/datosabiertos/def/urbanismo-infraestructuras/direccionPostal
Administrative units: http://vocab.linkeddata.es/datosabiertos/def/sector-publico/territorio

Streets

Vocabulary: http://vocab.linkeddata.es/datosabiertos/def/urbanismo-infraestructuras/callejero
SKOS: http://vocab.linkeddata.es/datosabiertos/kos/urbanismo-infraestructuras/tipo-via

Tourism

Vocabulary: http://vocab.linkeddata.es/datosabiertos/def/turismo/lugar

Cultural agenda

Vocabulary: http://vocab.linkeddata.es/datosabiertos/def/cultura-ocio/agenda

Shop census

Vocabulary: http://vocab.linkeddata.es/datosabiertos/def/comercio/tejidoComercial
SKOS: http://vocab.linkeddata.es/page/datosabiertos/kos/comercio/cnae

Population

Vocabulary: http://www.w3.org/TR/vocab-data-cube/
SKOS Codelistso Age: http://eurostat.linked-statistics.org/dic/age.rdf
o Gender: http://eurostat.linked-statistics.org/dic/sex.rdf
o Geo: http://eurostat.linked-statistics.org/dic/geo.rdf

BudgetVocabulary: http://vocab.linkeddata.es/datosabiertos/def/hacienda/presupuesto

Public tendering

Vocabulary: http://contsem.unizar.es/def/sector-publico/pproc

Air quality

Vocabulary: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn

Traffic

Vocabulary: http://vocab.linkeddata.es/datosabiertos/def/transporte/trafico

Public transport

Vocabulary: http://vocab.linkeddata.es/datosabiertos/def/transporte/transportePublico

Parking

Vocabulary:
http://vocab.linkeddata.es/datosabiertos/def/urbanismo-infraestructuras/aparcamiento

OjoAlData100• http://ojoaldata100.okfn.es/

Which are our main challenges?
• What should we publish?
• Added value for cities• Added value for reusers• Available legislation• How should we publish it?
• In which format(s)?
• With which level of detail?

Our decision
Let’s work towards creating arich catalogue of datasets(around 100), with a wide rangeof stakeholders and following abottom-up approach
Thanks to…

Acknowledgements
Santiago Mota
Olga Quirós
Eva Méndez
Esther Minguela

Antonio Ibáñez

Nancy Gómez

Enrique Crespo

Alberto Abella

Oscar
CorchoAndrés Recio

Guadalupe Miñana
Victoria López… And all those who participatedin the UNE178301 technical norm

Open Data Guide by FEMP

http://datos.gob.es/es/noticia/la-femp-elabora-una-guia-de-datos-abiertos-para-ayuntamientos-y-entidades-locales

Building Agreed Vocabularies andData Structures for Successful
Open City Data Sharing
The need to scale up...

Ontology Development Methods and Methodologies
1995

Grüninger
& Fox

1996

1997

METHONTOLOGY

...

2001

2002

On-ToKnowledge
Guide 101

2003

2004

2005

2006

...

2009

DILIGENT
EXtremeOntology

2010

NeOn

RapidOWL

XD• Towards lightweight and agile processes• Inspiration from software development practices• Coupling Software and ontology development

O. Development Methodologies

Ontology Development Lightweight Approaches
110Ontology engineering tools @OEG-UPM§ Ontology evaluationo OOPS! OntOlogy Pitfall Scanner
http://oops.linkeddata.es/§ Vocabulary documentationo HTML: Widoco
https://github.com/dgarijo/Widoco/o Diagrams: AR2DTool
http://ar2dtool.linkeddata.es/o Vocabulary registry•••

OEG vocabularies
http://vocab.linkeddata.es/
Smart Cities: http://smartcity.linkeddata.es/
Open Data:
http://vocab.linkeddata.es/datosabiertos/o Vocabulary distributed developmento OnToology http://ontoology.linkeddata.es/

111

Ontology development process overview§ Ont. Devel.

§ Ont. Devel.
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implementation

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maintenance

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specification
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questions

Legend§ ActorActivity

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115Requirement specification

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116Implementation - Conceptualization
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wot:hasValueType(0..1)
wot:DataType117

Implementation - Encoding
Openly managed
in GitHub

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https://github.com/mariapoveda/vicinity-ontology-wot
118

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120

Ontology development process overview
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GitHub issue tracker:
new needs, bugs, etc.

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Aiming at bringing all this together…
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Technology support for this activity
Evaluation reports

HTML documentation

Diagrams

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Technology support for this activity
Registration
based onPublication
integrates

integrates integrates

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integrates

Using OnToology

Add repository to track

125

Using OnToology

List of user repositories registered in OnToology

126

Using OnToology

RDF files management by repository127

Using OnToology

When the ontology (RDF file) is updated, regenerate:

Or when forcing the generation of resources

128

Using OnToology

Publish the ontology under a w3id URI

Or download the resources needed to publish it in your server129

In summary…
• Open data offers good opportunities to cities• For 3rd party reusers• For city officers• But these efforts need to be taken seriously in orderto make the most of it• We need a principled approach to publish and exploit
open data more effectively• Not only based on open formats and 5-star classifications• But also on shared vocabularies and data structures• Develop once, deploy everywhereNext steps• Continue improving technical infrastructure• Much of it available at https://github.com/oeg-upm/• Create approx. 30 ontologies in the context of the“Ciudades Abiertas” (Open Cities) project• Create working groups for specific datasets withdomain experts, users and ontology engineers• Similarly to what is being done with OpenContracting,
Open311, etc.

Ontology Engineering at Scale forOpen City Data Sharing
The 8th Joint International Semantic
Technology Conference (JIST2018)
Awaji City, Hyogo, Japan, 27/11/2018
With contributions from María Poveda, Raúl García-Castro
and Paola Espinoza
Oscar Corcho
ocorcho@fi.upm.es@ocorcho, @opencitydata_es
https://www.slideshare.com/ocorcho