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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 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
  • 2. 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
  • 3. 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
  • 4. NGG
    CG
    PhenomenOntology, hydrOntology
    Step 1: Building PhenomenOntology
    Step 2: Mappings between the catalogues and the Ontology
    BCN200
    BCN25
    Our main goal: Data Integration
  • 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. Differentproducershavedifferentvocabularies
  • 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. 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
  • 12. 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
  • 13. 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.
  • 14. NeOn Methodology
    Process and activities covered:
    • Ontology Specification
    • 15. Scheduling
    • 16. Non Ontological Resource Reuse
    • 17. Non Ontological Resource Reengineering
    • 18. Reuse General Ontologies
    • 19. Reuse Domain Ontologies
    • 20. Reuse Ontology Statements
    • 21. Reuse Ontology Design Patterns
    All processes and activities are described with:
  • 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
  • 24. HydrontologyDevelopment
    NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse
    María del Carmen Suárez de Figueroa Baonza
  • 25. 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
  • 26. INSPIRE - Annexes
    Luis Manuel Vilches Blázquez
  • 27. 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
  • 28. 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
  • 29. 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
  • 30. 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
  • 31. Modellingthehydrologydomain
    150+ classes, 47 object properties, 64 data properties and 256 axioms.
  • 32. 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
  • 33. PhenomenontologyDevelopment
    NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse
    María del Carmen Suárez de Figueroa Baonza
  • 34. Knowledge Bases
    Numerical Cartographic Database (BCN200)
    Numerical Cartographic Database (BCN25)
    Conciso Gazetteer
    National Geographic Gazetteer
  • 35. 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
  • 36. 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)
  • 37.
    • 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
  • 38. 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
    • 39. 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)
  • 40. BCN25  BTN25
    Base Cartográfica N. (BCN25)
  • 41. BCN25  PhenomenOntology v3.5
    03 ¿?
    0301 Río
    0304 Cauce artificial
  • HomogeneisingURIs and labels
    Exploiting “type” hierarchies
    Reducingunnecessaryattributes
    Incorporating BTN25 definitions as rdfs:comments
    Ontology curation
    Luis Manuel Vilches Blázquez
  • 63. 35
    Ontological Engineering Group
    HomogeneisingURIs and labels
    • Meaninglesslabelsfromthefirstlevel in thehierarchy
  • HomogeneisingURIs and labels
    • Allclass and propertynames in lowercase
    36
    Ontological Engineering Group
  • 64. 37
    Ontological Engineering Group
    HomogeneisingURIs and labels
    • Spaces and accents in URIs
  • Exploiting “type” hierarchies
    Attribute “type” normallycorrespondstoadditionaltaxonomies
    38
    Ontological Engineering Group
  • 65. Reducingunnecessary/redundantattributes
    39
    Ontological Engineering Group
  • 66. 40
    Ontological Engineering Group
    Completingdocumentation
  • 67. Somestatistics(from BCN25 to BTN25)
    PhenomenOntology 4.0
    PhenomenOntology 3.6
  • 68. 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
  • 69. 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
  • 70. 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
  • 71. 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
  • 72. 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)
  • 73. 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
  • 74. Linked Open Data evolution
  • LOD clouds
  • 77. Linked Open Data Evolution
    50
  • 78. 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.
  • 79. 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
  • 80. 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
  • 81. 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:
    • 82. B. Obama –Transparency and Open Government
    • 83. T. Berners-Lee - Raw data now!
    • 84. J. Manuel Alonso - ¿Qué es Open Data?
    • 85. Open Government Data
    • 86. 8 Principles of Open Government Data
  • Open Government. USA and UK
    55
    BOTTOM-UP
    Top-down
  • 87. Linked Data Mashup (data.gov)
    Clean Air Status and Trends (CASTNET)
    http://data-gov.tw.rpi.edu/demo/exhibit/demo-8-castnet.php
  • 88. 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
  • 89. 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
  • 90. 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
  • 91. Related Work
  • 92. 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
  • 93. 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
  • 94. 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
  • 95. 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
  • 96. 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
  • 97. 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
  • 98. 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
  • 99. 2. Vocabularydevelopment: HydrOntology
    68
    Asunción Gómez Pérez
  • 100. 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
  • 101. 3. Generation of the RDF Data
    NOR2O
    INE
    ODEMapster
    IGN
    Geometry2RDF
    Geospatial
    column
    IGN
  • 102. 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
  • 103. 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
  • 104. NOR2O Modules
    73
  • 105. 3. Generation of the RDF Data – NOR2O
    NOR2O
    Year
    Industry Production Index
    Province
  • 106. 3. Generation of the RDF Data – R2O & ODEMapster
    Creationand execution of R2O Mappings
    Checkout at http://www.neon-toolkit.org/
  • 107. 3. Generation of the RDF Data
  • 108. 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'
  • 109. 3. Generation of the RDF Data – Geometry2RDF
  • 110. 3. Generation of the RDF Data – Geometry2RDF
  • 111. 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
  • 112. 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
  • 113. 4. Publication of the RDF Data
  • 114. 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
  • 115. 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
  • 116. 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
  • 117. 6. Linking of the RDF Data
    http://geo.linkeddata.es/page/Provincia/Granada
    86
    Asunción Gómez Pérez
  • 118. 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
  • 119. 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
  • 120. Provinces
  • 121. IndustryProductionIndex – Capital of Province
  • 122. Rivers
  • 123. Beaches
  • 124. 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.
  • 125. 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
  • 126. 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
  • 127. 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