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

    • Experiences in theDevelopment of Geographical Ontologies and Linked Data
      OntoGeoWorkhop, Toulouse, 18 November 2010
      Oscar Corcho, Luis Manuel Vilches Blázquez, José Angel Ramos Gargantilla {ocorcho,lmvilches,jramos}@fi.upm.es
      Ontology EngineeringGroup, 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 manyothers
      WorkdistributedunderthelicenseCreativeCommonsAttribution-Noncommercial-Share Alike 3.0
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • NGG
      CG
      PhenomenOntology, hydrOntology
      Step 1: Building PhenomenOntology
      Step 2: Mappings between the catalogues and the Ontology
      BCN200
      BCN25
      Our main goal: Data Integration
    • 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
    • Differentproducershavedifferentvocabularies
    • 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
    • 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
      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
    • 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
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • Knowledge Resources
      Ontological Resources
      O. Design Patterns
      4
      3
      O. Repositories and Registries
      5
      6
      Flogic
      RDF(S)
      OWL
      Ontological Resource
      Reuse
      O. Aligning
      O. Merging
      5
      6
      2
      Ontology Design
      Pattern Reuse
      4
      Non Ontological Resource
      Reuse
      3
      6
      Non Ontological Resources
      Ontological Resource
      Reengineering
      2
      7
      Glossaries
      Dictionaries
      Lexicons
      5
      NeOn Scenarios
      Non Ontological Resource
      Reengineering
      4
      6
      Classification
      Schemas
      Thesauri
      Taxonomies
      Alignments
      2
      RDF(S)
      1
      Flogic
      O. Specification
      O. Formalization
      O. Implementation
      O. Conceptualization
      OWL
      8
      Ontology Restructuring
      (Pruning, Extension,
      Specialization, Modularization)
      9
      O. Localization
      1,2,3,4,5,6,7,8, 9
      Ontology Support Activities: Knowledge Acquisition (Elicitation); Documentation;
      Configuration Management; Evaluation (V&V); Assessment
    • NeOnScenarios
      Building ontology networks from scratch without reusing existing resources.
      Building ontology networks by reusing and reengineering non ontological resources.
      Building ontology networks by reusing ontologies or ontology modules.
      Building ontology networks by reusing and reengineeringontologies or ontology modules.
      Building ontology networks by reusing and merging ontology or ontology modules.
      Building ontology networks by reusing, merging and reengineeringontologies or ontology modules.
      Building ontology networks by reusing ontology design patterns.
      Building ontology networks by restructuring ontologies or ontology modules.
      Building ontology networks by localizing ontologies or ontology modules.
    • 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
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • HydrontologyDevelopment
      NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse
      María del Carmen Suárez de Figueroa Baonza
    • One of the INSPIRE aimsistoharmoniseGeographicalinformationsourcestogive support toformulating, implementing and evaluating EU policies (e.g., Environmental Management).
      GeographicalInformationSources: Databasesfrom EU StateMembers at local, regional, national and international levels.
      INSPIRE as a contextforhydrontology
      Luis Manuel Vilches Blázquez
    • INSPIRE - Annexes
      Luis Manuel Vilches Blázquez
    • Information Sources
      Thesauri and Bibliography
      Feature Catalogues
      Getty
      FTT ADL
      BCN25
      GEMET
      WFD
      CC.AA.
      EGM & ERM
      Dictionaries and Monographs
      BCN200
      Nomenclátor Geográfico Nacional
      Nomenclátor Conciso
      Luis Manuel Vilches Blázquez
    • 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
    • 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
    • Ontology Development
      WGS84 Geo Positioning: an RDF vocabulary
      scv:Dimension
      scv:Item
      scv:Dataset
      hydrographicalphenomena (rivers, lakes, etc.)
      Vocabulary for instants, intervals, durations, etc.
      Names and international code systems for territories and groups
      Ontology for OGC Geography Markup Language
    • Modellingthehydrologydomain
      150+ classes, 47 object properties, 64 data properties and 256 axioms.
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • PhenomenontologyDevelopment
      NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse
      María del Carmen Suárez de Figueroa Baonza
    • Knowledge Bases
      Numerical Cartographic Database (BCN200)
      Numerical Cartographic Database (BCN25)
      Conciso Gazetteer
      National Geographic Gazetteer
    • Knowledge Bases
      Conciso Gazetteer
      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.
      Gazetteer is a directory of instances of a class or classes of features than contain some information regarding position (ISO 19112)
      National Geographic Gazetteer
    • 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)
      • Code: 3 pair of digits
      XXYYZZ
      060101
      06 Transportation
      01 Roads
      01 Highway. Axis
      • Name:
      Highway. Axis
      Highway under construction. Axis
      ...
      Catalogue columns:
      • Group:
      0- unfixed
      1- road
      ...
      BCN25 details
    • Bottom-up process: PhenomenOntology
      PhenomenOntology
      Automatic ontology building from BCN25/BTN25
      BCN25/BTN25
      • Automatic checking of linguistic differences (linsearch): plurals, punctuation marks, capital letters and Spanish signs
      • Curationprocess by expert domain of IGN-E
    • 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)
    • BCN25  BTN25
      Base Cartográfica N. (BCN25)
    • BCN25  PhenomenOntology v3.5
      03 ¿?
      0301 Río
      0304 Cauce artificial
      • 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
    • HomogeneisingURIs and labels
      Exploiting “type” hierarchies
      Reducingunnecessaryattributes
      Incorporating BTN25 definitions as rdfs:comments
      Ontology curation
      Luis Manuel Vilches Blázquez
    • 35
      Ontological Engineering Group
      HomogeneisingURIs and labels
      • Meaninglesslabelsfromthefirstlevel in thehierarchy
    • HomogeneisingURIs and labels
      • Allclass and propertynames in lowercase
      36
      Ontological Engineering Group
    • 37
      Ontological Engineering Group
      HomogeneisingURIs and labels
      • Spaces and accents in URIs
    • Exploiting “type” hierarchies
      Attribute “type” normallycorrespondstoadditionaltaxonomies
      38
      Ontological Engineering Group
    • Reducingunnecessary/redundantattributes
      39
      Ontological Engineering Group
    • 40
      Ontological Engineering Group
      Completingdocumentation
    • Somestatistics(from BCN25 to BTN25)
      PhenomenOntology 4.0
      PhenomenOntology 3.6
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • Generic ontology developmentmethodologies can beappliedwithsomesuccess
      Hydrontologytook a total of 6PM approximately
      Initially done by a domainexpertafterveryinitial training
      Ontology debuggingwasextremelydifficult and has providedinterestingresults in thisarea
      Top down vs bottom up approaches
      Largecurationprocessstillneeded in bottom-up approaches, whichmaynotadvisefollowingit (researchongoing on this)
      More lightweightontologieswithbottom-up approach, althougheasierto relate tounderlying catalogues
      Nextsteps on relatingthemtoupper-levelontologies (e.g., Dolce) and modularisingforimprovingreusability
      Someconclusions in ontology development
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • Whatisthe 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 machineprocessable descriptions of its meaning.
      And technologies and infrastructure to do this
      And clear principles on how to publish data
      data
    • 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)
    • The fourprinciples (Tim Berners Lee, 2006)
      Use URIs as names for things
      Use HTTP URIs so that people can look up those names.
      When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL)
      Include links to other URIs, so that they can discover more things.
      http://www.w3.org/DesignIssues/LinkedData.html
      47
      http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html
    • Linked Open Data evolution
      • 2007
      • 2008
      • 2009
    • LOD clouds
    • Linked Open Data Evolution
      50
    • Howshouldwepublish data?
      Formats in which data ispublishednowadays…
      XML
      HTML
      DBs
      APIs
      CSV
      XLS

      However, mainlimitationsfrom a Web of Data point of view
      Difficulttointegrate
      Data isnotlinkedtoeachother, as ithappenswith Web documents.
    • How do wepublishLinked Data?
      ExposingRelationalDatabasesorother similar formatsintoLinked Data
      D2R
      Triplify
      R2O
      NOR2O
      Virtuoso
      Ultrawrap

      Usingnative RDF triplestores
      Sesame
      Jena
      Owlim
      Talisplatform

      Incorporatingit in theform of RDFa in CMSslikeDrupal
      52
    • 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)
      Listing on this slide by T. Heath, M. Hausenblas, C. Bizer, R. Cyganiak, O. Hartig
      53
    • Oneadditionalmotivation: 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
    • Open Government. USA and UK
      55
      BOTTOM-UP
      Top-down
    • Linked Data Mashup (data.gov)
      Clean Air Status and Trends (CASTNET)
      http://data-gov.tw.rpi.edu/demo/exhibit/demo-8-castnet.php
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • 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
    • Motivation
      99.171 % English
      0.019 % Spanish
      The Web of Data ismainlyfor
      Englishspeakers
      Poorpresence of Spanish
      Source:Billion Triples dataset at http://km.aifb.kit.edu/projects/btc-2010/
      Thanks to Aidan and Richard
    • Related Work
    • 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
      61
      Asunción Gómez Pérez
    • Processfor Publishing Linked Data onthe Web
      Identification
      of the data sources
      Vocabulary
      development
      Generation
      of the RDF Data
      Publication
      of the RDF data
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
    • 1. Identification and selection of the data sources
      Identification
      of the data sources
      Instituto GeográficoNacional
      Vocabulary
      development
      Generation
      of the RDF Data
      Publication
      of the RDF data
      Basque
      Catalan
      Galician
      Spanish
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
    • 1. Identification and selection of the data sources
      Identification
      of the data sources
      Instituto Nacionalde Estadística
      Vocabulary
      development
      Generation
      of the RDF Data
      Year
      Publication
      of the RDF data
      Data cleansing
      Province
      Linking
      the RDF data
      Enable effective
      discovery
    • 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
      Thisisnotenough
      Publication
      of the RDF data
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
    • 2. Vocabularydevelopment
      Features
      Lightweight :
      Taxonomies and a fewproperties
      Consensuatedvocabularies
      Toavoidthemappingproblems
      Multilingual
      Linked data are multilingual
      TheNeOnmethodology can helpto
      Re-enginer Non ontologicalresourcesintoontologies
      Pros: use domainterminologyalreadyconsensuatedbydomainexperts
      Withdraw in heavyweightontologiesthosefeaturesthatyoudon’tneed
      Reuseexistingvocabularies
      66
      Identification
      of the data sources
      Vocabulary
      development
      Generation
      of the RDF Data
      Publication
      of the RDF data
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
      Asunción Gómez Pérez
    • Vocabularydevelopment: Specification
      Content requirements: Identifythe set of questionsthattheontologyshouldanswer
      Whichone are theprovinces in Spain?
      Where are thebeaches?
      Where are thereservoirs?
      Identifytheproductionindex in Madrid
      Whichoneisthecitywithhigherproductionindex?
      Give me Madrid latitude and altitude
      ….
      Non-contentrequirements
      Theontologymustbe in thefourofficialSpanishlanguages
      67
      Asunción Gómez Pérez
    • 2. Vocabularydevelopment: HydrOntology
      68
      Asunción Gómez Pérez
    • 3. Generation of RDF
      Fromthe Data sources
      Geographicinformation (Databases)
      Statisticinformation(spreadsheets)
      Geospatialinformation
      Differenttechnologiesfor RDF generation
      Reengineering patterns
      R20 and ODEMapster
      Geometrygeneration
      Identification
      of the data sources
      Vocabulary
      development
      Generation
      of the RDF Data
      Publication
      of the RDF data
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
    • 3. Generation of the RDF Data
      NOR2O
      INE
      ODEMapster
      IGN
      Geometry2RDF
      Geospatial
      column
      IGN
    • 3. Generation of the RDF Data
      Preliminaries
      SelectappropriateURIs
      Difficulties
      CumbersomeURIsin Spanish
      http://geo.linkeddata.es/ontology/Río
      RDF allows UTF-8 charactersforURIs
      But, Linked Data URIs has tobeURLs as well
      So, non ASCII-US charactershavetobe %code
      http://geo.linkeddata.es/ontology/R%C3%ADo
    • 3. Generation of the RDF Data / instances
      NOR2O is a software librarythatimplementsthetransformationsproposedbythePatternsfor Re-engineering Non-OntologicalResources (PR-NOR). Currentlywehave 16 PR-NORs.
      PR-NORs define a procedurethattransforms a Non-OntologicalResource (NOR) componentsintoontologyelements. http://ontologydesignpatterns.org/
      · Classificationschemes
      NOR2O
      · Thesauri
      · Lexicons
      NOR2O
      FAO Water classification
      · Classification scheme
      · Path enumeration data model
      · Implemented in a database
    • NOR2O Modules
      73
    • 3. Generation of the RDF Data – NOR2O
      NOR2O
      Year
      Industry Production Index
      Province
    • 3. Generation of the RDF Data – R2O & ODEMapster
      Creationand execution of R2O Mappings
      Checkout at http://www.neon-toolkit.org/
    • 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))
      AS Gml311Geometry
      FROM "BCN200"."BCN200_0301L_RIO" c
      WHERE c.Etiqueta='Arroyo'
    • 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 NamedGraphs
      BTN25
      BCN200
      IPI
      ….
      http://geo.linkeddata.es/dataset/IGN/BTN25
      http://geo.linkeddata.es/dataset/IGN/BCN200
      http://geo.linkeddata.es/dataset/INE/IPI
    • 4. Publication of the RDF Data
      Identification
      of the data sources
      Vocabulary
      development
      SPARQL
      Linked Data
      HTML
      Generation
      of the RDF Data
      IncludingProvenance
      Support
      Publication
      of the RDF data
      Pubby
      Pubby 0.3
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
      Virtuoso 6.1.0
    • 4. Publication of the RDF Data
    • 4. Publication of the RDF Data - License
      Data Licenses
      Officiallicense as published in theSpanishofficialjournal (BOE - Boletín Oficial del Estado)
      CreativeCommonsoptions
      GNU Free DocumentationLicense
      Eachdatasethas itsownspecificlicense
      IGN
      INE
    • 5. Data cleansing
      Identification
      of the data sources
      Lack of documentation of the IGN datasets
      Broken links: Spain, IGN resources
      Lack of documentation of theontology
      Missingenglish and spanishlabels
      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.
      Vocabulary
      development
      Generation
      of the RDF Data
      Publication
      of the RDF data
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
    • 6. Linking of the RDF Data
      Identification
      of the data sources
      Silk - A Link Discovery Framework for the Web of Data
      First set of links: Provinces of Spain
      86% accuracy
      Vocabulary
      development
      Geonames
      Generation
      of the RDF Data
      GeoLinkedData
      DBPedia
      Publication
      of the RDF data
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
    • 6. Linking of the RDF Data
      http://geo.linkeddata.es/page/Provincia/Granada
      86
      Asunción Gómez Pérez
    • 7. Enable effective discovery
      Identification
      of the data sources
      Vocabulary
      development
      Generation
      of the RDF Data
      Publication
      of the RDF data
      Data cleansing
      Linking
      the RDF data
      Enable effective
      discovery
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage inhttp://geo.linkeddata.es/
      Structure of my Talk
    • Provinces
    • IndustryProductionIndex – Capital of Province
    • Rivers
    • Beaches
    • 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.
    • WhydidwestartdevelopingGeographical Ontologies?
      Methodologicalguidelinesfor ontology development
      The NeOnMethodology
      The developmentprocessforHydrontology
      The developmentprocessforPhenomenOntology
      WhydidwestartdevelopingGeographical Linked Data?
      Methodologicalguidelinesfor Linked Data generation
      Ontology and Linked Data usage in http://geo.linkeddata.es/
      Structure of my Talk
    • Reusable ontologiesavailableforthe community
      Well-founded and welldocumented
      Nowworking on multilinguality/multiculturalityissues
      Workcontinuing in understandinghowtoprovidedebuggingtoolsfordomainexperts.
      Reusable toolsforgeospatial Linked Data generation
      Thereisstill a lack of understanding of howmuchbenefitwe can getfrom Linked Geographical Data
      Benefits of linkingseemtobeclear
      Butgeo-processingisstillunsolved in RDF, as well as geometryrepresentation
      General conclusions
      Luis Manuel Vilches Blázquez
    • Experiences in theDevelopment of Geographical Ontologies and Linked Data
      OntoGeoWorkhop, Toulouse, 18 November 2010
      Oscar Corcho, Luis Manuel Vilches Blázquez, José Angel Ramos Gargantilla {ocorcho,lmvilches,jramos}@fi.upm.es
      Ontology EngineeringGroup, 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 manyothers
      WorkdistributedunderthelicenseCreativeCommonsAttribution-Noncommercial-Share Alike 3.0