0
Experiences in the
Development of
Geographical Ontologies
and Linked Data
OntoGeo Workhop, Toulouse, 18 November 2010
Osca...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
CG
NGG
BCN200
BCN25
PhenomenOntology, hydrOntology
Our main goal: Data Integration
Step 1: Building
PhenomenOntology
Step ...
• Great variety of sources
• Near 20 different producers in Spain (national and local
cartographic institutions with diffe...
Different producers have different vocabularies
• Great variety of sources
• Various degrees of quality and structuring of
information
• ICC has 49 types of features in t...
Feature Catalogues
Base Cartográfica N. (BCN200)
Base Cartográfica N. (BCN25)
• Great variety of sources
• Various degrees of quality and structuring of
information
• Natural language ambiguity
• Syno...
• Great variety of sources
• Various degrees of quality and structuring of
information
• Natural language ambiguity
• Syno...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
O. Specification O. Conceptualization O. ImplementationO. Formalization
1
RDF(S)
OWL
Flogic
NeOn Scenarios
Ontology Restru...
NeOn Scenarios
1. Building ontology networks from scratch without reusing existing
resources.
2. Building ontology network...
NeOn Methodology
Process and activities covered:
 Ontology Specification
 Scheduling
 Non Ontological Resource Reuse
 ...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
Hydrontology Development
NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse
María del Ca...
• One of the INSPIRE aims is to harmonise
Geographical information sources to give support to
formulating, implementing an...
INSPIRE - Annexes
Luis Manuel Vilches Blázquez
Information Sources
GEMET
Feature Catalogues
BCN25
BCN200
EGM & ERM
CC.AA.
Nomenclátor Geográfico Nacional
Thesauri and Bi...
• Glossary of hydrOntology terms.
• Feature Catalogues of the Numerical Cartographic Database
(1:25.000; 1:200.000; 1:1.00...
Criteria for structuring
• Abstracts concepts from:
• Water Framework Directive
• Proposed by the EU Parliament and EU Cou...
Ontology Development
hasStatisticalData
on
Ontology
Specification
Legend
hydrOntology
4
FAO
FAO
Geopolitical
ontology
WGS8...
Modelling the hydrology domain
Nivel superior
Nivel inferior
150+ classes, 47 object properties, 64 data properties and 25...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
Phenomenontology Development
NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse
María de...
Knowledge Bases
Conciso Gazetteer
National Geographic Gazetteer
Numerical Cartographic Database (BCN200)
Numerical Cartogr...
Knowledge Bases
• National Geographic Gazetteer
has 14 item types and 460,000
toponyms (Spanish, Galician,
Basque, Catalan...
Knowledge Bases
• BCN25 was designed as a derived
product from National
Topographic Map and this was
built to obtain carto...
Catalogue columns:
- Group:
0- unfixed
1- road
...
- Code: 3 pair of digits
XXYYZZ
060101
06 Transportation
01 Roads
01 Hi...
Bottom-up process: PhenomenOntology
• Automatic ontology building from
BCN25/BTN25
BCN25/BTN25
• Automatic checking of lin...
Criteria for taxonomy creation
• Group (Road, Hydrographic...)
• Code column
• (Topic) - (030501)
• (Group) – (030501)
• (...
BCN25  BTN25
Base Cartográfica N. (BCN25)
BCN25  PhenomenOntology v3.5
03 ¿?
- Componente de río
• Eje
• Margen
• Eje conexión
- Régimen
• Permanente
• No permanen...
• Homogeneising URIs and labels
• Exploiting “type” hierarchies
• Reducing unnecessary attributes
• Incorporating BTN25 de...
35Ontological Engineering Group
Homogeneising URIs and labels
- Meaningless labels from the first level in the hierarchy
36Ontological Engineering Group
Homogeneising URIs and labels
- All class and property names in lowercase
37Ontological Engineering Group
Homogeneising URIs and labels
- Spaces and accents in URIs
38Ontological Engineering Group
Exploiting “type” hierarchies
Attribute “type” normally corresponds to additional taxonomi...
39Ontological Engineering Group
Reducing unnecessary/redundant attributes
40Ontological Engineering Group
Completing documentation
Some statistics (from BCN25 to BTN25)
PhenomenOntology 4.0PhenomenOntology 3.6
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
• Generic ontology development methodologies can be
applied with some success
• Hydrontology took a total of 6PM approxima...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
What is the Web of Linked Data?
• An extension of the current
Web…
• … where information and services
are given well-defin...
What is Linked Data?
• Linked Data is a term used to describe a
recommended best practice for exposing, sharing,
and conne...
The four principles (Tim Berners Lee, 2006)
1. Use URIs as names
for things
2. Use HTTP URIs so
that people can look
up th...
Linked Open Data evolution
 2007
 2008
 2009
LOD clouds
Linked Open Data Evolution
50
How should we publish data?
• Formats in which data is published nowadays…
• XML
• HTML
• DBs
• APIs
• CSV
• XLS
• …
• How...
How do we publish Linked Data?
1. Exposing Relational Databases or other similar formats
into Linked Data
• D2R
• Triplify...
How do we consume Linked Data?
• Linked Data browsers
• To explore things and datasets and to navigate between them.
• Tab...
One additional motivation: Open Government
• Government and state administration should be
opened at all levels to effecti...
Open Government. USA and UK
55
Linked Data Mashup (data.gov)
• Clean Air Status and Trends (CASTNET)
• http://data-gov.tw.rpi.edu/demo/exhibit/demo-8-cas...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
GeoLinkedData
• It is an open initiative whose aim is to enrich the Web
of Data with Spanish geospatial data.
• This initi...
Motivation
» 99.171 % English
» 0.019 % Spanish
Source:Billion Triples dataset at http://km.aifb.kit.edu/projects/btc-2010...
Related Work
Impact of geo.linkeddata.es
• Number of triples in Spanish (July 2010): 1.412.248
• Number of triples in Spanish (Septembe...
Process for Publishing Linked Data on the Web
Identification
of the data sources
Vocabulary
development
Generation
of the ...
1. Identification and selection of the data sources
Instituto Geográfico
Nacional
Identification
of the data sources
Vocab...
1. Identification and selection of the data sources
Instituto Nacional
de Estadística
Identification
of the data sources
V...
2. Vocabulary development
http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/#whichvocabs
Identification
of the ...
2. Vocabulary development
• Features
• Lightweight :
• Taxonomies and a few properties
• Consensuated vocabularies
• To av...
Vocabulary development: Specification
• Content requirements: Identify the set of questions
that the ontology should answe...
2. Vocabulary development: HydrOntology
68Asunción Gómez Pérez
3. Generation of RDF
• From the Data
sources
• Geographic
information
(Databases)
• Statistic information
(spreadsheets)
•...
3. Generation of the RDF Data
INE
NOR2O
ODEMapster
IGN
IGN
Geospatial
column
Geometry2RDF
3. Generation of the RDF Data
• Preliminaries
• Select appropriate URIs
• Difficulties
• Cumbersome URIs in Spanish
• http...
3. Generation of the RDF Data / instances
• NOR2O is a software library that implements the transformations
proposed by th...
NOR2O Modules
73
3. Generation of the RDF Data – NOR2O
Industry Production Index
Province
Year
NOR2O
3. Generation of the RDF Data – R2O & ODEMapster
• Creation and execution of R2O Mappings
• Check out at http://www.neon-t...
3. Generation of the RDF Data
3. Generation of the RDF Data – Geometry2RDF
Oracle STO UTIL package
SELECT TO_CHAR(SDO_UTIL.TO_GML311GEOMETRY(geometry))
...
3. Generation of the RDF Data – Geometry2RDF
3. Generation of the RDF Data – Geometry2RDF
3. Generation of the RDF data – RDF graphs
• IGN INE
• So far
• 7 RDF Named Graphs
BTN25 BCN200 IPI….
http://geo.linkeddat...
4. Publication of the RDF Data
SPARQL
Pubby
Linked DataHTML
Virtuoso 6.1.0
Pubby 0.3
Including Provenance
Support
Identifi...
4. Publication of the RDF Data
4. Publication of the RDF Data - License
• Data Licenses
• Official license as published in the Spanish official journal
(...
5. Data cleansing
• Lack of documentation of the IGN datasets
• Broken links: Spain, IGN resources
• Lack of documentation...
6. Linking of the RDF Data
• Silk - A Link Discovery Framework for
the Web of Data
• First set of links: Provinces of Spai...
6. Linking of the RDF Data
• http://geo.linkeddata.es/page/Provincia/Granada
86Asunción Gómez Pérez
7. Enable effective discovery
Identification
of the data sources
Vocabulary
development
Generation
of the RDF Data
Publica...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
Provinces
Industry Production Index – Capital of Province
Rivers
Beaches
Future Work
• Generate more datasets from other domains, e.g.
universities in Spain.
• Identify more links to DBPedia and ...
• Why did we start developing Geographical
Ontologies?
• Methodological guidelines for ontology development
• The NeOn Met...
• Reusable ontologies available for the community
• Well-founded and well documented
• Now working on multilinguality/mult...
Experiences in the
Development of
Geographical Ontologies
and Linked Data
OntoGeo Workhop, Toulouse, 18 November 2010
Osca...
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Experiences in the Development of Geographical Ontologies and Linked Data

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Keynote at the OntoGeo workshop, held in Toulouse, France, on Nov 18th 2010, collocated with SAGEO2010.

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Transcript of "Experiences in the Development of Geographical Ontologies and Linked Data"

  1. 1. Experiences in the Development of Geographical Ontologies and Linked Data OntoGeo Workhop, Toulouse, 18 November 2010 Oscar Corcho, Luis Manuel Vilches Blázquez, José Angel Ramos Gargantilla {ocorcho,lmvilches,jramos}@fi.upm.es Ontology Engineering Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid Credits: Asunción Gómez-Pérez, María del Carmen Suárez de Figueroa, Boris Villazón, Alex de León, Víctor Saquicela, Miguel Angel García, Juan Sequeda and many others Work distributed under the license Creative Commons Attribution- Noncommercial-Share Alike 3.0
  2. 2. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  3. 3. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  4. 4. CG NGG BCN200 BCN25 PhenomenOntology, hydrOntology Our main goal: Data Integration Step 1: Building PhenomenOntology Step 2: Mappings between the catalogues and the Ontology
  5. 5. • Great variety of sources • Near 20 different producers in Spain (national and local cartographic institutions with different interest) • Various degrees of quality and structuring of information • Natural language ambiguity • Synonymy, polysemy and hyperonymy • Scale factor Why ontologies? Geographical Information Context
  6. 6. Different producers have different vocabularies
  7. 7. • Great variety of sources • Various degrees of quality and structuring of information • ICC has 49 types of features in total • IGN has (only in the hydrographic domain) 40 types of features • Natural language ambiguity • Synonymy, polysemy and hyperonymy • Scale factor Why ontologies? Geographical Information Context
  8. 8. Feature Catalogues Base Cartográfica N. (BCN200) Base Cartográfica N. (BCN25)
  9. 9. • Great variety of sources • Various degrees of quality and structuring of information • Natural language ambiguity • Synonymy: Different words with the same meaning » riverside, river bank • Polysemy: Same word with different meanings. Bank » Bank: Financial institution » Bank: Relay upon (trust) • Hyperonymy: One word includes other. » Bank and Morgan Bank • Scale factor Why ontologies? Geographical Information Context
  10. 10. • Great variety of sources • Various degrees of quality and structuring of information • Natural language ambiguity • Synonymy, polysemy and hyperonymy • Scale factor • E.g., one village may be represented as a point X,Y or as an area XN,YN • This can act as a filter for geographical information • Different scales normally present different features • Generalisation processes are normally a problem, due to the difficulties in finding “feature overlaps” in different feature catalogues Why ontologies? Geographical Information Context
  11. 11. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  12. 12. O. Specification O. Conceptualization O. ImplementationO. Formalization 1 RDF(S) OWL Flogic NeOn Scenarios Ontology Restructuring (Pruning, Extension, Specialization, Modularization) 8 O. Localization 9 Ontology Support Activities: Knowledge Acquisition (Elicitation); Documentation; Configuration Management; Evaluation (V&V); Assessment 1,2,3,4,5,6,7,8, 9 O. Aligning O. Merging Alignments5 5 5 Ontological Resource Reengineering 4 4 4 6 6 6 6 Knowledge Resources Ontological Resources O. Design Patterns 2 Non Ontological Resources Thesauri DictionariesGlossaries Lexicons Taxonomies Classification Schemas Non Ontological Resource Reuse Non Ontological Resource Reengineering 2 2 O. Repositories and Registries Flogic RDF(S) OWL Ontology Design Pattern Reuse 7 3 Ontological Resource Reuse 3
  13. 13. NeOn Scenarios 1. Building ontology networks from scratch without reusing existing resources. 2. Building ontology networks by reusing and reengineering non ontological resources. 3. Building ontology networks by reusing ontologies or ontology modules. 4. Building ontology networks by reusing and reengineering ontologies or ontology modules. 5. Building ontology networks by reusing and merging ontology or ontology modules. 6. Building ontology networks by reusing, merging and reengineering ontologies or ontology modules. 7. Building ontology networks by reusing ontology design patterns. 8. Building ontology networks by restructuring ontologies or ontology modules. 9. Building ontology networks by localizing ontologies or ontology modules.
  14. 14. NeOn Methodology Process and activities covered:  Ontology Specification  Scheduling  Non Ontological Resource Reuse  Non Ontological Resource Reengineering  Reuse General Ontologies  Reuse Domain Ontologies  Reuse Ontology Statements  Reuse Ontology Design Patterns All processes and activities are described with:  A filling card  A workflow  Examples
  15. 15. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  16. 16. Hydrontology Development NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse María del Carmen Suárez de Figueroa Baonza
  17. 17. • One of the INSPIRE aims is to harmonise Geographical information sources to give support to formulating, implementing and evaluating EU policies (e.g., Environmental Management). • Geographical Information Sources: Databases from EU State Members at local, regional, national and international levels. INSPIRE as a context for hydrontology Luis Manuel Vilches Blázquez
  18. 18. INSPIRE - Annexes Luis Manuel Vilches Blázquez
  19. 19. Information Sources GEMET Feature Catalogues BCN25 BCN200 EGM & ERM CC.AA. Nomenclátor Geográfico Nacional Thesauri and Bibliography WFD Nomenclátor Conciso Dictionaries and Monographs FTT ADL Getty Luis Manuel Vilches Blázquez
  20. 20. • Glossary of hydrOntology terms. • Feature Catalogues of the Numerical Cartographic Database (1:25.000; 1:200.000; 1:1.000.000) • Different Feature Catalogue from other local producers. • EuroGlobalMap & EuroRegionalMap • Water Framework Directive • Alexandria Digital Library, Dewey • Thesauri (UNESCO, GEMET, Getty Thesaurus of Geographic Names, etc.) • National Geographic Gazetteer • Bibliography (Dictionary, Water, Law, etc.) • This glossary contains more than 120 concepts
  21. 21. Criteria for structuring • Abstracts concepts from: • Water Framework Directive • Proposed by the EU Parliament and EU Council • List of hydrographic phenomena definition • Part of the model from: • SDIGER Project • INSPIRE pilot project • Two river basins, two countries, two languages • Several semantic criteria from: • WordNet • Encyclopaedia Britannica • Diccionario de la Real Academia de la Lengua • Wikipedia • Several domain references • Inheritance: From various actual catalogues • Meetings with domain experts that belong to IGN-E
  22. 22. Ontology Development hasStatisticalData on Ontology Specification Legend hydrOntology 4 FAO FAO Geopolitical ontology WGS84 4W3C Vocabulary GML 4GML Specification O. Statistics SCOVO O. Time W3C Time hasLat/Long hasGeometry hasLat/Long hasGeometry hasLocation/isLocated Thesaurus UNESCO 4EGM / ERM GeoNames … scv:Dimension scv:Item scv:Dataset WGS84 Geo Positioning: an RDF vocabulary hydrographical phenomena (rivers, lakes, etc.) Ontology for OGC Geography Markup Language Vocabulary for instants, intervals, durations, etc. Names and international code systems for territories and groups
  23. 23. Modelling the hydrology domain Nivel superior Nivel inferior 150+ classes, 47 object properties, 64 data properties and 256 axioms.
  24. 24. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  25. 25. Phenomenontology Development NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse María del Carmen Suárez de Figueroa Baonza
  26. 26. Knowledge Bases Conciso Gazetteer National Geographic Gazetteer Numerical Cartographic Database (BCN200) Numerical Cartographic Database (BCN25)
  27. 27. Knowledge Bases • National Geographic Gazetteer has 14 item types and 460,000 toponyms (Spanish, Galician, Basque, Catalan, and Aranes). • Conciso Gazetteer, which is agreed with the United Nations Conferences Recommendations on Geographic Names Normalization, has 17 item types and 3667 toponyms. Conciso Gazetteer • Gazetteer is a directory of instances of a class or classes of features than contain some information regarding position (ISO 19112) National Geographic Gazetteer
  28. 28. Knowledge Bases • BCN25 was designed as a derived product from National Topographic Map and this was built to obtain cartographic information that complies with the required data specifications exploited inside GIS. • BCN200 was developed through analogical map digitalisation of provincial maps. • Information is structured in 8 topics (Administrative boundaries, Relief, Hydrography, Vegetation and so on) • Feature catalogue presents the abstraction of reality, represented in one or more sets of geographic data, as a defined classification of phenomena (ISO 19110) Numerical Cartographic Database (BCN25) Numerical Cartographic Database (BCN200)
  29. 29. Catalogue columns: - Group: 0- unfixed 1- road ... - Code: 3 pair of digits XXYYZZ 060101 06 Transportation 01 Roads 01 Highway. Axis - Name: Highway. Axis Highway under construction. Axis ... BCN25 details
  30. 30. Bottom-up process: PhenomenOntology • Automatic ontology building from BCN25/BTN25 BCN25/BTN25 • Automatic checking of linguistic differences (linsearch): plurals, punctuation marks, capital letters and Spanish signs • Curation process by expert domain of IGN-E PhenomenOntology
  31. 31. Criteria for taxonomy creation • Group (Road, Hydrographic...) • Code column • (Topic) - (030501) • (Group) – (030501) • (Subgroup) – (030501) • Common lexical parts • Highway with 2 lines • Highway with 3 lines • Highway under construction • Highway (superclass) • Lexical heterogeneity in feature names (“Autovía”, “AUTOVIA”, “Autovia”, “Autovía-”) Numerical Cartographic Database (BCN25)
  32. 32. BCN25  BTN25 Base Cartográfica N. (BCN25)
  33. 33. BCN25  PhenomenOntology v3.5 03 ¿? - Componente de río • Eje • Margen • Eje conexión - Régimen • Permanente • No permanente - Categoría del río • Desconocida • Primera • Segunda • Tercera • Cuarta - Componente del cauce artificial • Eje • Margen • Eje conexión - Situación • Desconocido • Subterráneo • Superficial • Elevado 0301 Río 0304 Cauce artificial
  34. 34. • Homogeneising URIs and labels • Exploiting “type” hierarchies • Reducing unnecessary attributes • Incorporating BTN25 definitions as rdfs:comments Ontology curation Luis Manuel Vilches Blázquez
  35. 35. 35Ontological Engineering Group Homogeneising URIs and labels - Meaningless labels from the first level in the hierarchy
  36. 36. 36Ontological Engineering Group Homogeneising URIs and labels - All class and property names in lowercase
  37. 37. 37Ontological Engineering Group Homogeneising URIs and labels - Spaces and accents in URIs
  38. 38. 38Ontological Engineering Group Exploiting “type” hierarchies Attribute “type” normally corresponds to additional taxonomies
  39. 39. 39Ontological Engineering Group Reducing unnecessary/redundant attributes
  40. 40. 40Ontological Engineering Group Completing documentation
  41. 41. Some statistics (from BCN25 to BTN25) PhenomenOntology 4.0PhenomenOntology 3.6
  42. 42. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  43. 43. • Generic ontology development methodologies can be applied with some success • Hydrontology took a total of 6PM approximately • Initially done by a domain expert after very initial training • Ontology debugging was extremely difficult and has provided interesting results in this area • Top down vs bottom up approaches • Large curation process still needed in bottom-up approaches, which may not advise following it (research ongoing on this) • More lightweight ontologies with bottom-up approach, although easier to relate to underlying catalogues • Next steps on relating them to upper-level ontologies (e.g., Dolce) and modularising for improving reusability Some conclusions in ontology development
  44. 44. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  45. 45. What is the Web of Linked Data? • An extension of the current Web… • … where information and services are given well-defined and explicitly represented meaning, … • … so that it can be shared and used by humans and machines, ... • ... better enabling them to work in cooperation • How? • Promoting information exchange by tagging web content with machine processable descriptions of its meaning. • And technologies and infrastructure to do this • And clear principles on how to publish data data
  46. 46. What is Linked Data? • Linked Data is a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF. • Part of the Semantic Web • Exposing, sharing and connecting data • Technologies: URIs and RDF (although others are also important)
  47. 47. The four principles (Tim Berners Lee, 2006) 1. Use URIs as names for things 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) 4. Include links to other URIs, so that they can discover more things. • http://www.w3.org/D esignIssues/Linked Data.html 47 http://www.ted.com/talks/tim_berners_lee_on_the_next_web.htm
  48. 48. Linked Open Data evolution  2007  2008  2009
  49. 49. LOD clouds
  50. 50. Linked Open Data Evolution 50
  51. 51. How should we publish data? • Formats in which data is published nowadays… • XML • HTML • DBs • APIs • CSV • XLS • … • However, main limitations from a Web of Data point of view • Difficult to integrate • Data is not linked to each other, as it happens with Web documents.
  52. 52. How do we publish Linked Data? 1. Exposing Relational Databases or other similar formats into Linked Data • D2R • Triplify • R2O • NOR2O • Virtuoso • Ultrawrap • … 2. Using native RDF triplestores • Sesame • Jena • Owlim • Talis platform • … 3. Incorporating it in the form of RDFa in CMSs like Drupal 52
  53. 53. How do we consume Linked Data? • Linked Data browsers • To explore things and datasets and to navigate between them. • Tabulator Browser (MIT, USA), Marbles (FU Berlin, DE), OpenLink RDF Browser (OpenLink, UK), Zitgist RDF Browser (Zitgist, USA), Disco Hyperdata Browser (FU Berlin, DE), Fenfire (DERI, Ireland) • Linked Data mashups • Sites that mash up (thus combine Linked data) • Revyu.com (KMI, UK), DBtune Slashfacet (Queen Mary, UK), DBPedia Mobile (FU Berlin, DE), Semantic Web Pipes (DERI, Ireland) • Search engines • To search for Linked Data. • Falcons (IWS, China), Sindice (DERI, Ireland), MicroSearch (Yahoo, Spain), Watson (Open University, UK), SWSE (DERI, Ireland), Swoogle (UMBC, USA) 53 Listing on this slide by T. Heath, M. Hausenblas, C. Bizer, R. Cyganiak, O. Hartig
  54. 54. One additional motivation: Open Government • Government and state administration should be opened at all levels to effective public scrutiny and oversight • Objectives: • Transparency • Participation • Collaboration • Inclusion • Cost reduction • Interoperability • Reusability • Leadership • Market & Value 54 •Some Links: • B. Obama –Transparency and Open Government • T. Berners-Lee - Raw data now! • J. Manuel Alonso - ¿Qué es Open Data? • Open Government Data • 8 Principles of Open Government Data
  55. 55. Open Government. USA and UK 55
  56. 56. Linked Data Mashup (data.gov) • Clean Air Status and Trends (CASTNET) • http://data-gov.tw.rpi.edu/demo/exhibit/demo-8-castnet.php
  57. 57. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  58. 58. GeoLinkedData • It is an open initiative whose aim is to enrich the Web of Data with Spanish geospatial data. • This initiative has started off by publishing diverse information sources, such as National Geographic Institute of Spain (IGN-E) and National Statistics Institute (INE) • http://geo.linkeddata.es
  59. 59. Motivation » 99.171 % English » 0.019 % Spanish Source:Billion Triples dataset at http://km.aifb.kit.edu/projects/btc-2010/ Thanks to Aidan and Richard The Web of Data is mainly for English speakers Poor presence of Spanish
  60. 60. Related Work
  61. 61. Impact of geo.linkeddata.es • Number of triples in Spanish (July 2010): 1.412.248 • Number of triples in Spanish (September 2010): 21.463.088 61Asunción Gómez Pérez Before geo.linkeddata.es en 99,1712875 ja 0,463849377 fr 0,05447229 de 0,034225134 pl 0,02532934 it 0,021982542 es 0,019584648 After geo.linkeddata.es en 94,18744941 es 5,044085342 ja 0,440538697 fr 0,051734793 de 0,032505155 pl 0,024056418 it 0,020877812
  62. 62. Process for Publishing Linked Data on the Web Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  63. 63. 1. Identification and selection of the data sources Instituto Geográfico Nacional Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery Basque Catalan Galician Spanish
  64. 64. 1. Identification and selection of the data sources Instituto Nacional de Estadística Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery Province Year
  65. 65. 2. Vocabulary development http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/#whichvocabs Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  66. 66. 2. Vocabulary development • Features • Lightweight : • Taxonomies and a few properties • Consensuated vocabularies • To avoid the mapping problems • Multilingual • Linked data are multilingual • The NeOn methodology can help to • Re-enginer Non ontological resources into ontologie • Pros: use domain terminology already consensuated by domain experts • Withdraw in heavyweight ontologies those features that you don’t need • Reuse existing vocabularies 66Asunción Gómez Pérez Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  67. 67. Vocabulary development: Specification • Content requirements: Identify the set of questions that the ontology should answer • Which one are the provinces in Spain? • Where are the beaches? • Where are the reservoirs? • Identify the production index in Madrid • Which one is the city with higher production index? • Give me Madrid latitude and altitude • …. • Non-content requirements • The ontology must be in the four official Spanish languages 67Asunción Gómez Pérez
  68. 68. 2. Vocabulary development: HydrOntology 68Asunción Gómez Pérez
  69. 69. 3. Generation of RDF • From the Data sources • Geographic information (Databases) • Statistic information (spreadsheets) • Geospatial information • Different technologies for RDF generation • Reengineering patterns • R20 and ODEMapster • Geometry generation Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  70. 70. 3. Generation of the RDF Data INE NOR2O ODEMapster IGN IGN Geospatial column Geometry2RDF
  71. 71. 3. Generation of the RDF Data • Preliminaries • Select appropriate URIs • Difficulties • Cumbersome URIs in Spanish • http://geo.linkeddata.es/ontology/Río • RDF allows UTF-8 characters for URIs • But, Linked Data URIs has to be URLs as well • So, non ASCII-US characters have to be %code • http://geo.linkeddata.es/ontology/R%C3%ADo
  72. 72. 3. Generation of the RDF Data / instances • NOR2O is a software library that implements the transformations proposed by the Patterns for Re-engineering Non-Ontological Resources (PR-NOR). Currently we have 16 PR-NORs. • PR-NORs define a procedure that transforms a Non-Ontological Resource (NOR) components into ontology elements. http://ontologydesignpatterns.org/ NOR2O · Classification schemes · Thesauri · Lexicons NOR2O FAO Water classification · Classification scheme
  73. 73. NOR2O Modules 73
  74. 74. 3. Generation of the RDF Data – NOR2O Industry Production Index Province Year NOR2O
  75. 75. 3. Generation of the RDF Data – R2O & ODEMapster • Creation and execution of R2O Mappings • Check out at http://www.neon-toolkit.org/
  76. 76. 3. Generation of the RDF Data
  77. 77. 3. Generation of the RDF Data – Geometry2RDF Oracle STO UTIL package SELECT TO_CHAR(SDO_UTIL.TO_GML311GEOMETRY(geometry)) AS Gml311Geometry FROM "BCN200"."BCN200_0301L_RIO" c WHERE c.Etiqueta='Arroyo'
  78. 78. 3. Generation of the RDF Data – Geometry2RDF
  79. 79. 3. Generation of the RDF Data – Geometry2RDF
  80. 80. 3. Generation of the RDF data – RDF graphs • IGN INE • So far • 7 RDF Named Graphs BTN25 BCN200 IPI…. http://geo.linkeddata.es/dataset/IGN/BTN25 http://geo.linkeddata.es/dataset/IGN/BCN200 http://geo.linkeddata.es/dataset/INE/IPI
  81. 81. 4. Publication of the RDF Data SPARQL Pubby Linked DataHTML Virtuoso 6.1.0 Pubby 0.3 Including Provenance Support Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  82. 82. 4. Publication of the RDF Data
  83. 83. 4. Publication of the RDF Data - License • Data Licenses • Official license as published in the Spanish official journal (BOE - Boletín Oficial del Estado) • Creative Commons options • GNU Free Documentation License • Each dataset has its own specific license • IGN • INE
  84. 84. 5. Data cleansing • Lack of documentation of the IGN datasets • Broken links: Spain, IGN resources • Lack of documentation of the ontology • Missing english and spanish labels • Building a spanish ontology and importing some concepts of other ontology (in English): • Importing the English ontology. Add annotations like a Spanish label to them. • Importing the English ontology, creating new concepts and properties with a Spanish name and map those to the English equivalents. • Re-declaring the terms of the English ontology that we need (using the same URI as in the English ontology), and adding a Spanish label. • Creating your own class and properties that model the same things as the English ontology. Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  85. 85. 6. Linking of the RDF Data • Silk - A Link Discovery Framework for the Web of Data • First set of links: Provinces of Spain • 86% accuracy GeoLinkedDataDBPedia Geonames Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  86. 86. 6. Linking of the RDF Data • http://geo.linkeddata.es/page/Provincia/Granada 86Asunción Gómez Pérez
  87. 87. 7. Enable effective discovery Identification of the data sources Vocabulary development Generation of the RDF Data Publication of the RDF data Linking the RDF data Data cleansing Enable effective discovery
  88. 88. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  89. 89. Provinces
  90. 90. Industry Production Index – Capital of Province
  91. 91. Rivers
  92. 92. Beaches
  93. 93. Future Work • Generate more datasets from other domains, e.g. universities in Spain. • Identify more links to DBPedia and Geonames. • Cover complex geometrical information, i.e. not only Point and LineString-like data; we will also treat information representation through polygons.
  94. 94. • Why did we start developing Geographical Ontologies? • Methodological guidelines for ontology development • The NeOn Methodology • The development process for Hydrontology • The development process for PhenomenOntology • Why did we start developing Geographical Linked Data? • Methodological guidelines for Linked Data generation • Ontology and Linked Data usage in http://geo.linkeddata.es/ Structure of my Talk
  95. 95. • Reusable ontologies available for the community • Well-founded and well documented • Now working on multilinguality/multiculturality issues • Work continuing in understanding how to provide debugging tools for domain experts. • Reusable tools for geospatial Linked Data generation • There is still a lack of understanding of how much benefit we can get from Linked Geographical Data • Benefits of linking seem to be clear • But geo-processing is still unsolved in RDF, as well as geometry representation General conclusions Luis Manuel Vilches Blázquez
  96. 96. Experiences in the Development of Geographical Ontologies and Linked Data OntoGeo Workhop, Toulouse, 18 November 2010 Oscar Corcho, Luis Manuel Vilches Blázquez, José Angel Ramos Gargantilla {ocorcho,lmvilches,jramos}@fi.upm.es Ontology Engineering Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid Credits: Asunción Gómez-Pérez, María del Carmen Suárez de Figueroa, Boris Villazón, Alex de León, Víctor Saquicela, Miguel Angel García, Juan Sequeda and many others Work distributed under the license Creative Commons Attribution- Noncommercial-Share Alike 3.0
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