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Ontology Engineering at Scale for Open City Data Sharing

The document discusses the challenges and strategies associated with ontology engineering for open city data sharing, emphasizing the importance of agreed vocabularies and shared data structures. It outlines various technical and organizational barriers to adopting open data principles in Spanish cities, and highlights relevant legislation and the current state of open data portals. Key strategies for successful open city data initiatives include thoughtful dataset selection, semantic annotations, and creating standardized data formats for easier access and reuse.

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Ontology Engineering at Scale for Open City Data Sharing School of Informatics, The University of Edinburgh 13/05/2019 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
License • This work is licensed under the license CC BY-NC-SA 4.0 International • http://purl.org/NET/rdflicense/cc-by-nc-sa4.0 • You are free: • to Share — to copy, distribute and transmit the work • to Remix — to adapt the work • Under the following conditions • Attribution — You must attribute the work by inserting • “[source Oscar Corcho]” at the footer of each reused slide • a credits slide stating: “These slides are partially based on “Ontology Engineering at Scale for Open City Data Sharing” by O. Corcho” • Non-commercial • Share-Alike
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 a difference • 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 for Open City Data Sharing Building Agreed Vocabularies and Data Structures for Successful Open City Data Sharing
Building Agreed Vocabularies and Data 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, to the requirement to attribute and sharealike • Key aspects: • Availability and access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the Internet. The data must also 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, re- use 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 • Convention on official documentation access (2009). 12 countries • PSI-reuse Directives. (2003/98/EC, 2013/37/UE and upcoming one based on agreement on 22/01/2019) • https://ec.europa.eu/digital-single-market/en/european-legislation-reuse-public-sector-information • 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 good quality 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)
 Non reusable √ Reusable.  Not open √ Reusable, open.  Incomplete √ Reusable, open, complete  Not linked √ Reusable, open, complete, linked Data representation formats And many more: JSON, JSON-LD, Marc21, Shapefiles, KMZ, KML, PC-Axis, etc.
Building Agreed Vocabularies and Data 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 the generation 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 to municipalities • 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, Rivas- Vaciamadrid, Arganda del Rey) • Also national (http://datos.gob.es/) and regional (Aragón, Comunidad de Madrid, CRTM)
Project Ciudades Abiertas (funded by red.es) INICIATIVA PLATAFORMA DE GOBIERNO ABIERTO, COLABORATIVA E INTEROPERABLE (121/17-SP) July 2018 – June 2020 More details at http://www.ciudadesabiertas.es/ (Spanish)
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 (and growing) • 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 and Provinces - FEMP (may 2014) • http://www.femp.es/files/11-5133- fichero/Ordenanza%20Transparencia,%20Acceso%20y %20Reutilizaci%C3%B3n%20de%20la%20informaci%C 3%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-herramientas- publicacion-od_vfinal.pdf
Building Agreed Vocabularies and Data Structures for Successful Open City Data Sharing
Building Agreed Vocabularies and Data Structures for Successful Open City Data Sharing
Main challenges for success • Many efforts so far focused on setting up the technology infrastructure • That’s ok, but content is probably more important • Heterogeneity on the selection of datasets, their formats 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 and which 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-wide organisations
Rule 1. Chop your onions appropriately • 11 datasets selected • Based on frequency of requests from reusers • Slow but steady adoption for 2015 and 2016 • And now working on extending it to 100 datasets • With an additional group of people Datasets Cultural Agenda Traffic Population Streets Public Transport Touristic Places and POIs Budget Shop Census Air Quality Contracts Parkings
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 can understand 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 the benefits of open data (and Citizen Science) and how they can contribute to improving 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 your food 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 and Data Structures for Successful Open City Data Sharing
Building Agreed Vocabularies and Data Structures for Successful Open City Data Sharing Whom of you has never ever eaten a burger in his/her life? If you are vegetarian or vegan, 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 same data structures Publish Extract Publish Extract Publish Extract I want to publish my data 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… https://github.com/opencitydata More on this later...
So, are we ready to start cooking our open data better?
Open Data as a socio-technical problem • As you may have seen already, it is not all about technology, but mostly a socio-technical challenge The path towards establishing these agreements...
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-wide organisations
UNE 178301:2015. Objectives • Establish metrics and indicators that allow evaluating the degree of maturity of open data elaborated or curated by the public sector… • … so that we can facilitate reuse, • … • … and define a minimal list of datasets that should be 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 Corcho • PArticipants 1. Animsa. Iñigo Sanciñena 2. Animsa. Jesús Vera 3. Ayuntamiento de Albacete. Manuel Tobarra 4. 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. Etra. Vicente Sebastián 20. FEMP. Pablo Bárcenas 21. Geoactio. Pello Gámez 22. Gmv. Antonio Velasco 23. Indra. Elisabet Terrades 24. Indra. Jordi Marín 25. Junta de Castilla y León. Antonio Ibáñez 26. MINETUR. Ana Pérez 27. MINETUR. Javier García 28. MINHAP. Aleida Alcaide 29. Proconsi. Constantino Lázaro 30. Red.es. José Ignacio Sánchez 31. Red.es. Sonia Castro 32. SEGITTUR. Calixto Mellen 33. SEGITTUR. Enrique Lancis 34. SEGITTUR. Rosa Muñoz 35. SEGITTUR. Susana García 36. Universidad de Zaragoza. Javier López
Strategy Strategy Legal Legal Organisational Organisation Measurements Technical Availability Access Data Quality Update Socio-economic Reuse Participation and Collaboration UNE178301:2015. Metrics Domains Dimensions Raw data Completeness Documentation Correctness Geo-referencing Linked Data
UNE 178301. Metrics. Example
UNE178301:2015. High-Priority Datasets • Listing based on the current inventories from all cities (and regions) • Harmonisation • Votes according to PSI- reuse requests Datasets Cultural Agenda Traffic Population Streets Public Transport Touristic Places and POIs Budget Shop Census Air Quality Contracts Parkings
Dataset Vocabulary General – shared across 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 Codelists o 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 Budget Vocabulary: 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 UNE 178301:2015. Vocabularies
OjoAlData100 • http://ojoaldata100.okfn.es/ Let’s work towards creating a rich catalogue of datasets (around 100), with a wide range of stakeholders and following a bottom-up approach
OjoAlData100: challenges and questions • Main questions to be answered to build the data catalogue • 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?
Alberto Abella Oscar Corcho Esther Minguela Victoria López Andrés Recio Guadalupe Miñana Antonio Ibáñez Eva Méndez Nancy Gómez Santiago Mota Enrique Crespo … And all those who participated in the UNE178301 technical norm Olga Quirós OjoAlData100: Contributors (non exhaustive)
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 and Data Structures for Successful Open City Data Sharing The need to scale up...
Common patterns for ontology engineering • Ongoing systematic process to discuss data models with city information officers, and obtain common patterns P. Espinoza-Arias, M. Poveda-Villalón, R. García-Castro, O. Corcho, Ontological Representation of Smart City Data: From Devices to Cities, Appl. Sci. 9(1) (2019) 32
Ontology Development Methods and Methodologies 62 • Towards lightweight and agile processes • Inspiration from software development practices • Coupling Software and ontology development
A new wave of ontology engineering tools @OEG 63  Ontology evaluation o OOPS! OntOlogy Pitfall Scanner http://oops.linkeddata.es/  Ontology documentation o HTML: Widoco https://github.com/dgarijo/Widoco/ o Diagrams: AR2DTool http://ar2dtool.linkeddata.es/ o Ontology registry • OEG vocabularies http://vocab.linkeddata.es/ • Open Data: http://vocab.linkeddata.es/datosabiertos/ http://vocab.ciudadesabiertas.es/ o Ontology distributed development o OnToology http://ontoology.linkeddata.es/
Ontology development process overview 64 Legend Activity Actor Output activity flow Ontological requirements specification Ontology implementation Competency questions  Ont. Devel.  Ont. Devel.  Users  Experts Ontology maintenance Issue tracker  Ont. Devel.  Users  Experts Ontology Ontology Publication Online ontology  Ont. Devel.
Requirement specification 65 Requirement specification Ontologica l req uirements comp letion Comp etency q uestions (verified ) § Ont. Devel. § Users § Exp erts Use ca se sp ecifica tion Use ca ses § Ont. Devel. § Users § Exp erts Da ta excha nge id entifica tion Doma in d ocumenta tion Purp ose a nd scop e id entifica tion Ontologica l req uirements p rop osa l ORSD forma liza tion Ontology p urp ose a nd scop e Comp etency q uestions (ea rly sta ge) ORSD d ocument § Ont. Devel. § Users § Exp erts § Ont. Devel. § [Users] § Ont. Devel. ✓ ✓ ✓ § Users § Exp erts Ontology imp lementa tion § Ont. Devel. Ontology ma intena nce Cha nge req uests § Ont. Devel. § Users § Exp erts Ontology Ontology Pub lica tion Online ontology § Ont. Devel. https://lists.w3.org/Archives/Public/public-wot-ig/2016Dec/0016.html http://w3c.github.io/wot/current-practices/wot-practices
Requirement specification 66 Requirement specification Ontologica l req uirements comp letion Comp etency q uestions (verified ) § Ont. Devel. § Users § Exp erts Use ca se sp ecifica tion Use ca ses § Ont. Devel. § Users § Exp erts Da ta excha nge id entifica tion Doma in d ocumenta tion Purp ose a nd scop e id entifica tion Ontologica l req uirements p rop osa l ORSD forma liza tion Ontology p urp ose a nd scop e Comp etency q uestions (ea rly sta ge) ORSD d ocument § Ont. Devel. § Users § Exp erts § Ont. Devel. § [Users] § Ont. Devel. ✓ ✓ ✓ § Users § Exp erts Ontology imp lementa tion § Ont. Devel. Ontology ma intena nce Cha nge req uests § Ont. Devel. § Users § Exp erts Ontology Ontology Pub lica tion Online ontology § Ont. Devel. Shared in online spreadsheets snapshot in documentation
Implementation - Conceptualization 67 Implementation Ontologica l req uirements sp ecifica tion Encod ing Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology cod e Ontology Pub lica tion Online ontology § Ont. Devel. Ontology concep tua liza tion Ontology mod el § Ont. Devel. Eva lua tion § Ont. Devel. Va lid a ted ontology ✓ ✓
Implementation - Encoding 68 Implementation Ontologica l req uirements sp ecifica tion Encod ing Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology cod e Ontology Pub lica tion Online ontology § Ont. Devel. Ontology concep tua liza tion Ontology mod el § Ont. Devel. Eva lua tion § Ont. Devel. Va lid a ted ontology ✓ ✓ GitHub repository https://github.com/mariapoveda/vicinity-ontology-wot Openly managed in GitHub
Implementation - Evaluation 69 Implementation Ontologica l req uirements sp ecifica tion Encod ing Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology cod e Ontology Pub lica tion Online ontology § Ont. Devel. Ontology concep tua liza tion Ontology mod el § Ont. Devel. Eva lua tion § Ont. Devel. Va lid a ted ontology ✓ ✓ Online and notifications in GitHub repository https://github.com/mariapoveda/vicinity-ontology-wot Ongoing work: tests from requirements
Publication 70 Ontologypublication Ontologica l req uirements sp ecifica tion Ontology imp lementa tion Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology Prop ose relea se ca nd id a te Ontology § Ont. Devel. Documenta tion HTML d ocumenta tion § Ont. Devel. Online p ub lica tion Online a c cess (content negotia tion) § Ont. Devel. • HTML generation from OWL code • Multilingual • Separated sections + Diagrams + Descriptions + Examples • Own URI • purl, w3id, etc. • Content negotiation • Registry
Ontology development process overview 71 Ontological requirements specification Ontology implementation Competency questions  Ont. Devel.  Ont. Devel.  Users  Experts Ontology maintenance Issue tracker  Ont. Devel.  Users  Experts Ontology Ontology Publication Online ontology  Ont. Devel. Openly reported in GitHub issue tracker: new needs, bugs, etc.
Ontology development process overview 72 Ontological requirements specification Ontology implementation Competency questions  Ont. Devel.  Ont. Devel.  Users  Experts Ontology maintenance Issue tracker  Ont. Devel.  Users  Experts Ontology Ontology Publication Online ontology  Ont. Devel. Openly reported in GitHub issue tracker: new needs, bugs, etc. Aiming at bringing all this together…
Technology support for this activity Evaluation reports HTML documentation Diagrams Permanent ids Content negotiation Bundle Previsualization
Technology support for this activity based on Evaluation Documentation Publication Registration integrates integrates integratesintegrates
Using OnToology 75 Add repository to track
Using OnToology 76 List of user repositories registered in OnToology
Using OnToology 77 RDF files management by repository
Using OnToology 78 When the ontology (RDF file) is updated, regenerate: Or when forcing the generation of resources
Using OnToology 79 Publish the ontology under a w3id URI Or download the resources needed to publish it in your server
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 order to 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 • Not only based on technology for open data publication • 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 with domain experts, users and ontology engineers • Similarly to what is being done with OpenContracting, Open311, etc.
Ontology Engineering at Scale for Open City Data Sharing School of Informatics, The University of Edinburgh 13/05/2019 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

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Ontology Engineering at Scale for Open City Data Sharing

  • 1.
    Ontology Engineering atScale for Open City Data Sharing School of Informatics, The University of Edinburgh 13/05/2019 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
  • 2.
    License • This workis licensed under the license CC BY-NC-SA 4.0 International • http://purl.org/NET/rdflicense/cc-by-nc-sa4.0 • You are free: • to Share — to copy, distribute and transmit the work • to Remix — to adapt the work • Under the following conditions • Attribution — You must attribute the work by inserting • “[source Oscar Corcho]” at the footer of each reused slide • a credits slide stating: “These slides are partially based on “Ontology Engineering at Scale for Open City Data Sharing” by O. Corcho” • Non-commercial • Share-Alike
  • 3.
    Talk Objectives • Sharesome 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 a difference • Small bias towards how we are doing it in Spain • And how we are adapting existing ontology engineering practices to achieve this goal
  • 4.
    Ontology Engineering atScale for Open City Data Sharing Building Agreed Vocabularies and Data Structures for Successful Open City Data Sharing
  • 5.
    Building Agreed Vocabulariesand Data Structures for Successful Open City Data Sharing
  • 6.
    What is OpenData? • Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike • Key aspects: • Availability and access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the Internet. The data must also 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, re- use 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/ ]
  • 7.
    Relevant Legislation. Europeand Spain • Open Access Initiative (2001). Scientific information; > 510 orgs • Aarhus Convention (1998). Right to participate and access; 41 countries and the EU • Convention on official documentation access (2009). 12 countries • PSI-reuse Directives. (2003/98/EC, 2013/37/UE and upcoming one based on agreement on 22/01/2019) • https://ec.europa.eu/digital-single-market/en/european-legislation-reuse-public-sector-information • 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 good quality 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)]
  • 8.
    An Explosion ofOpen Data Portals
  • 9.
    Open Data andhow 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)
  • 10.
    Open Data andhow to publish it On a Web page or mobile app (for human consumption) Adapted from: Antonio Rodríguez Pascual (IGN)
  • 11.
    Open Data andhow 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)
  • 12.
    Open Data andhow 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)
  • 13.
    Open Data andhow 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)
  • 14.
  • 15.
    Open Data andhow to publish it Using semantically-enhanced and linked APIs • Also useful for developers • Standardised formats (JSON, JSON-LD, RDF) • Standardised models (vocabularies, ontologies)
  • 16.
     Non reusable √Reusable.  Not open √ Reusable, open.  Incomplete √ Reusable, open, complete  Not linked √ Reusable, open, complete, linked Data representation formats And many more: JSON, JSON-LD, Marc21, Shapefiles, KMZ, KML, PC-Axis, etc.
  • 17.
    Building Agreed Vocabulariesand Data Structures for Successful Open City Data Sharing
  • 18.
    Why our interestin cities • Regional and national datasets are useful to provide indicators, comparisons across countries, etc. • However, local datasets are usually better for the generation of economical and/or social value • Closer to people • Closer to businesses • Closer to developers
  • 19.
    Why our interestin cities • Node of the Open Data Institute for Madrid • Spin-off focused on providing open data support to municipalities • 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, Rivas- Vaciamadrid, Arganda del Rey) • Also national (http://datos.gob.es/) and regional (Aragón, Comunidad de Madrid, CRTM)
  • 20.
    Project Ciudades Abiertas(funded by red.es) INICIATIVA PLATAFORMA DE GOBIERNO ABIERTO, COLABORATIVA E INTEROPERABLE (121/17-SP) July 2018 – June 2020 More details at http://www.ciudadesabiertas.es/ (Spanish)
  • 21.
    The current statusof Open City Data in Spain Source: CTIC and OKFN
  • 22.
    Open Data Portalsin Spain • Around 60 open data portals for Spanish cities (and growing) • 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.
  • 23.
    Relevant legislation forcities • 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 and Provinces - FEMP (may 2014) • http://www.femp.es/files/11-5133- fichero/Ordenanza%20Transparencia,%20Acceso%20y %20Reutilizaci%C3%B3n%20de%20la%20informaci%C 3%B3n.pdf • 2015. UNE 178301. Ciudades Inteligentes. Datos abiertos (Open Data). MINETUR Y AENOR • More about this to come later in this presentation
  • 24.
    Technology that isused • 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-herramientas- publicacion-od_vfinal.pdf
  • 25.
    Building Agreed Vocabulariesand Data Structures for Successful Open City Data Sharing
  • 26.
    Building Agreed Vocabulariesand Data Structures for Successful Open City Data Sharing
  • 27.
    Main challenges forsuccess • Many efforts so far focused on setting up the technology infrastructure • That’s ok, but content is probably more important • Heterogeneity on the selection of datasets, their formats 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”
  • 28.
    Rule 1. Chop youronions appropriately
  • 29.
    Rule 1. Chopyour 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 and which datasets should I publish?
  • 30.
    Rule 1. Chopyour 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-wide organisations
  • 31.
    Rule 1. Chopyour onions appropriately • 11 datasets selected • Based on frequency of requests from reusers • Slow but steady adoption for 2015 and 2016 • And now working on extending it to 100 datasets • With an additional group of people Datasets Cultural Agenda Traffic Population Streets Public Transport Touristic Places and POIs Budget Shop Census Air Quality Contracts Parkings
  • 32.
    Rule 2. Add somespices, but not too many
  • 33.
    Rule 2. Addsome spices, but not too many • Annotate (semantically) your data, so that others can understand 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.)
  • 34.
    Rule 3. Trydifferent ways of plating up your food
  • 35.
    Rule 3. Trydifferent 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
  • 36.
    Rule 4. Let childrenappreciate (and cook) open data
  • 37.
    Rule 4. Letchildren appreciate (and cook) open data Let children understand the benefits of open data (and Citizen Science) and how they can contribute to improving the data of their city
  • 38.
    Rule 5…. Eat yourown dog food
  • 39.
    Let’s better say… Rule5. Try it out yourself first… … Before giving your food to your customers
  • 40.
    Rule 5. Tryit 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
  • 41.
    Building Agreed Vocabulariesand Data Structures for Successful Open City Data Sharing
  • 42.
    Building Agreed Vocabulariesand Data Structures for Successful Open City Data Sharing Whom of you has never ever eaten a burger in his/her life? If you are vegetarian or vegan, we can think of something else
  • 43.
    Rule 6. Fast foodhas 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..
  • 44.
    Rule 6. Fastfood has its value as well, why not… • When we open our data, let’s use at least the same data structures Publish Extract Publish Extract Publish Extract I want to publish my data I am using GTFS I am using my own CSV structure I provide it as a Web service Write an app and deploy everywhere
  • 45.
    Rule 6. Fastfood has its value as well, why not… https://github.com/opencitydata More on this later...
  • 46.
    So, are weready to start cooking our open data better?
  • 47.
    Open Data asa socio-technical problem • As you may have seen already, it is not all about technology, but mostly a socio-technical challenge The path towards establishing these agreements...
  • 48.
    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-wide organisations
  • 49.
    UNE 178301:2015. Objectives •Establish metrics and indicators that allow evaluating the degree of maturity of open data elaborated or curated by the public sector… • … so that we can facilitate reuse, • … • … and define a minimal list of datasets that should be published by all open city data initiatives, together with their associated vocabularies
  • 50.
    UNE 178301:2015. Theteam • 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
  • 51.
    UNE 178301:2015. Theteam • Coordinators • Localidata. Esther Minguela y Oscar Corcho • PArticipants 1. Animsa. Iñigo Sanciñena 2. Animsa. Jesús Vera 3. Ayuntamiento de Albacete. Manuel Tobarra 4. 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. Etra. Vicente Sebastián 20. FEMP. Pablo Bárcenas 21. Geoactio. Pello Gámez 22. Gmv. Antonio Velasco 23. Indra. Elisabet Terrades 24. Indra. Jordi Marín 25. Junta de Castilla y León. Antonio Ibáñez 26. MINETUR. Ana Pérez 27. MINETUR. Javier García 28. MINHAP. Aleida Alcaide 29. Proconsi. Constantino Lázaro 30. Red.es. José Ignacio Sánchez 31. Red.es. Sonia Castro 32. SEGITTUR. Calixto Mellen 33. SEGITTUR. Enrique Lancis 34. SEGITTUR. Rosa Muñoz 35. SEGITTUR. Susana García 36. Universidad de Zaragoza. Javier López
  • 52.
    Strategy Strategy Legal Legal Organisational Organisation Measurements Technical Availability Access Data Quality Update Socio-economic Reuse Participation andCollaboration UNE178301:2015. Metrics Domains Dimensions Raw data Completeness Documentation Correctness Geo-referencing Linked Data
  • 53.
  • 54.
    UNE178301:2015. High-Priority Datasets •Listing based on the current inventories from all cities (and regions) • Harmonisation • Votes according to PSI- reuse requests Datasets Cultural Agenda Traffic Population Streets Public Transport Touristic Places and POIs Budget Shop Census Air Quality Contracts Parkings
  • 55.
    Dataset Vocabulary General –shared across 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 Codelists o 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 Budget Vocabulary: 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 UNE 178301:2015. Vocabularies
  • 56.
    OjoAlData100 • http://ojoaldata100.okfn.es/ Let’s worktowards creating a rich catalogue of datasets (around 100), with a wide range of stakeholders and following a bottom-up approach
  • 57.
    OjoAlData100: challenges andquestions • Main questions to be answered to build the data catalogue • 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?
  • 58.
    Alberto Abella Oscar Corcho Esther Minguela VictoriaLópez Andrés Recio Guadalupe Miñana Antonio Ibáñez Eva Méndez Nancy Gómez Santiago Mota Enrique Crespo … And all those who participated in the UNE178301 technical norm Olga Quirós OjoAlData100: Contributors (non exhaustive)
  • 59.
    Open Data Guideby FEMP http://datos.gob.es/es/noticia/la-femp-elabora-una-guia-de-datos-abiertos-para-ayuntamientos-y-entidades-locales
  • 60.
    Building Agreed Vocabulariesand Data Structures for Successful Open City Data Sharing The need to scale up...
  • 61.
    Common patterns forontology engineering • Ongoing systematic process to discuss data models with city information officers, and obtain common patterns P. Espinoza-Arias, M. Poveda-Villalón, R. García-Castro, O. Corcho, Ontological Representation of Smart City Data: From Devices to Cities, Appl. Sci. 9(1) (2019) 32
  • 62.
    Ontology Development Methodsand Methodologies 62 • Towards lightweight and agile processes • Inspiration from software development practices • Coupling Software and ontology development
  • 63.
    A new waveof ontology engineering tools @OEG 63  Ontology evaluation o OOPS! OntOlogy Pitfall Scanner http://oops.linkeddata.es/  Ontology documentation o HTML: Widoco https://github.com/dgarijo/Widoco/ o Diagrams: AR2DTool http://ar2dtool.linkeddata.es/ o Ontology registry • OEG vocabularies http://vocab.linkeddata.es/ • Open Data: http://vocab.linkeddata.es/datosabiertos/ http://vocab.ciudadesabiertas.es/ o Ontology distributed development o OnToology http://ontoology.linkeddata.es/
  • 64.
    Ontology development processoverview 64 Legend Activity Actor Output activity flow Ontological requirements specification Ontology implementation Competency questions  Ont. Devel.  Ont. Devel.  Users  Experts Ontology maintenance Issue tracker  Ont. Devel.  Users  Experts Ontology Ontology Publication Online ontology  Ont. Devel.
  • 65.
    Requirement specification 65 Requirement specification Ontological req uirements comp letion Comp etency q uestions (verified ) § Ont. Devel. § Users § Exp erts Use ca se sp ecifica tion Use ca ses § Ont. Devel. § Users § Exp erts Da ta excha nge id entifica tion Doma in d ocumenta tion Purp ose a nd scop e id entifica tion Ontologica l req uirements p rop osa l ORSD forma liza tion Ontology p urp ose a nd scop e Comp etency q uestions (ea rly sta ge) ORSD d ocument § Ont. Devel. § Users § Exp erts § Ont. Devel. § [Users] § Ont. Devel. ✓ ✓ ✓ § Users § Exp erts Ontology imp lementa tion § Ont. Devel. Ontology ma intena nce Cha nge req uests § Ont. Devel. § Users § Exp erts Ontology Ontology Pub lica tion Online ontology § Ont. Devel. https://lists.w3.org/Archives/Public/public-wot-ig/2016Dec/0016.html http://w3c.github.io/wot/current-practices/wot-practices
  • 66.
    Requirement specification 66 Requirement specification Ontological req uirements comp letion Comp etency q uestions (verified ) § Ont. Devel. § Users § Exp erts Use ca se sp ecifica tion Use ca ses § Ont. Devel. § Users § Exp erts Da ta excha nge id entifica tion Doma in d ocumenta tion Purp ose a nd scop e id entifica tion Ontologica l req uirements p rop osa l ORSD forma liza tion Ontology p urp ose a nd scop e Comp etency q uestions (ea rly sta ge) ORSD d ocument § Ont. Devel. § Users § Exp erts § Ont. Devel. § [Users] § Ont. Devel. ✓ ✓ ✓ § Users § Exp erts Ontology imp lementa tion § Ont. Devel. Ontology ma intena nce Cha nge req uests § Ont. Devel. § Users § Exp erts Ontology Ontology Pub lica tion Online ontology § Ont. Devel. Shared in online spreadsheets snapshot in documentation
  • 67.
    Implementation - Conceptualization 67 Implementation Ontological req uirements sp ecifica tion Encod ing Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology cod e Ontology Pub lica tion Online ontology § Ont. Devel. Ontology concep tua liza tion Ontology mod el § Ont. Devel. Eva lua tion § Ont. Devel. Va lid a ted ontology ✓ ✓
  • 68.
    Implementation - Encoding 68 Implementation Ontological req uirements sp ecifica tion Encod ing Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology cod e Ontology Pub lica tion Online ontology § Ont. Devel. Ontology concep tua liza tion Ontology mod el § Ont. Devel. Eva lua tion § Ont. Devel. Va lid a ted ontology ✓ ✓ GitHub repository https://github.com/mariapoveda/vicinity-ontology-wot Openly managed in GitHub
  • 69.
    Implementation - Evaluation 69 Implementation Ontological req uirements sp ecifica tion Encod ing Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology cod e Ontology Pub lica tion Online ontology § Ont. Devel. Ontology concep tua liza tion Ontology mod el § Ont. Devel. Eva lua tion § Ont. Devel. Va lid a ted ontology ✓ ✓ Online and notifications in GitHub repository https://github.com/mariapoveda/vicinity-ontology-wot Ongoing work: tests from requirements
  • 70.
    Publication 70 Ontologypublication Ontologica l req uirements specifica tion Ontology imp lementa tion Comp etency q uestions § Ont. Devel. § Ont. Devel. § Users § Exp erts Ontology ma intena nce Issue tra cker § Ont. Devel. § Users § Exp erts Ontology Prop ose relea se ca nd id a te Ontology § Ont. Devel. Documenta tion HTML d ocumenta tion § Ont. Devel. Online p ub lica tion Online a c cess (content negotia tion) § Ont. Devel. • HTML generation from OWL code • Multilingual • Separated sections + Diagrams + Descriptions + Examples • Own URI • purl, w3id, etc. • Content negotiation • Registry
  • 71.
    Ontology development processoverview 71 Ontological requirements specification Ontology implementation Competency questions  Ont. Devel.  Ont. Devel.  Users  Experts Ontology maintenance Issue tracker  Ont. Devel.  Users  Experts Ontology Ontology Publication Online ontology  Ont. Devel. Openly reported in GitHub issue tracker: new needs, bugs, etc.
  • 72.
    Ontology development processoverview 72 Ontological requirements specification Ontology implementation Competency questions  Ont. Devel.  Ont. Devel.  Users  Experts Ontology maintenance Issue tracker  Ont. Devel.  Users  Experts Ontology Ontology Publication Online ontology  Ont. Devel. Openly reported in GitHub issue tracker: new needs, bugs, etc. Aiming at bringing all this together…
  • 73.
    Technology support forthis activity Evaluation reports HTML documentation Diagrams Permanent ids Content negotiation Bundle Previsualization
  • 74.
    Technology support forthis activity based on Evaluation Documentation Publication Registration integrates integrates integratesintegrates
  • 75.
  • 76.
    Using OnToology 76 List ofuser repositories registered in OnToology
  • 77.
    Using OnToology 77 RDF filesmanagement by repository
  • 78.
    Using OnToology 78 When theontology (RDF file) is updated, regenerate: Or when forcing the generation of resources
  • 79.
    Using OnToology 79 Publish theontology under a w3id URI Or download the resources needed to publish it in your server
  • 80.
    In summary… • Opendata offers good opportunities to cities • For 3rd party reusers • For city officers • But these efforts need to be taken seriously in order to 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 • Not only based on technology for open data publication • But also on shared vocabularies and data structures • Develop once, deploy everywhere
  • 81.
    Next steps • Continueimproving 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 with domain experts, users and ontology engineers • Similarly to what is being done with OpenContracting, Open311, etc.
  • 82.
    Ontology Engineering atScale for Open City Data Sharing School of Informatics, The University of Edinburgh 13/05/2019 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

Editor's Notes

  • #12 http://www.gtfs-data-exchange.com/agency/madrid/