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Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
Experiences in the Development of Geographical Ontologies and Linked Data
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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.

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

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  • 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. • 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. • 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. 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. • 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. Different producers have different vocabularies
  • 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. Feature Catalogues Base Cartográfica N. (BCN200) Base Cartográfica N. (BCN25)
  • 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. • 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. • 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. 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. 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. 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. • 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. Hydrontology Development NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse María del Carmen Suárez de Figueroa Baonza
  • 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. INSPIRE - Annexes Luis Manuel Vilches Blázquez
  • 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. • 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. 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. 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. Modelling the hydrology domain Nivel superior Nivel inferior 150+ classes, 47 object properties, 64 data properties and 256 axioms.
  • 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. Phenomenontology Development NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse María del Carmen Suárez de Figueroa Baonza
  • 26. Knowledge Bases Conciso Gazetteer National Geographic Gazetteer Numerical Cartographic Database (BCN200) Numerical Cartographic Database (BCN25)
  • 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. 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. 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. 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. 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. BCN25  BTN25 Base Cartográfica N. (BCN25)
  • 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. • Homogeneising URIs and labels • Exploiting “type” hierarchies • Reducing unnecessary attributes • Incorporating BTN25 definitions as rdfs:comments Ontology curation Luis Manuel Vilches Blázquez
  • 35. 35Ontological Engineering Group Homogeneising URIs and labels - Meaningless labels from the first level in the hierarchy
  • 36. 36Ontological Engineering Group Homogeneising URIs and labels - All class and property names in lowercase
  • 37. 37Ontological Engineering Group Homogeneising URIs and labels - Spaces and accents in URIs
  • 38. 38Ontological Engineering Group Exploiting “type” hierarchies Attribute “type” normally corresponds to additional taxonomies
  • 39. 39Ontological Engineering Group Reducing unnecessary/redundant attributes
  • 40. 40Ontological Engineering Group Completing documentation
  • 41. Some statistics (from BCN25 to BTN25) PhenomenOntology 4.0PhenomenOntology 3.6
  • 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. • 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. • 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. 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. 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. 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. Linked Open Data evolution  2007  2008  2009
  • 49. LOD clouds
  • 50. Linked Open Data Evolution 50
  • 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. 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. 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. 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. Open Government. USA and UK 55
  • 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. • 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. 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. 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. Related Work
  • 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. 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. 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. 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. 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. 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. 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. 2. Vocabulary development: HydrOntology 68Asunción Gómez Pérez
  • 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. 3. Generation of the RDF Data INE NOR2O ODEMapster IGN IGN Geospatial column Geometry2RDF
  • 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. 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. NOR2O Modules 73
  • 74. 3. Generation of the RDF Data – NOR2O Industry Production Index Province Year NOR2O
  • 75. 3. Generation of the RDF Data – R2O & ODEMapster • Creation and execution of R2O Mappings • Check out at http://www.neon-toolkit.org/
  • 76. 3. Generation of the RDF Data
  • 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. 3. Generation of the RDF Data – Geometry2RDF
  • 79. 3. Generation of the RDF Data – Geometry2RDF
  • 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. 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. 4. Publication of the RDF Data
  • 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. 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. 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. 6. Linking of the RDF Data • http://geo.linkeddata.es/page/Provincia/Granada 86Asunción Gómez Pérez
  • 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. • 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. Provinces
  • 90. Industry Production Index – Capital of Province
  • 91. Rivers
  • 92. Beaches
  • 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. • 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. • 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. 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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