A Geographic Knowledge A Geographic Knowledge Base for Semantic Web Base for Semantic Web Applications Applications Marcirio Silveira Chaves Mário J. Silva Bruno Martins 20º Brazilian Symposium on Databases - SBBD 2005 Uberlândia - MG Linguateca www.linguateca.pt
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A Geographic Knowledge Base for Semantic Web Applications
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A Geographic Knowledge Base A Geographic Knowledge Base for Semantic Web Applicationsfor Semantic Web ApplicationsA Geographic Knowledge Base A Geographic Knowledge Base for Semantic Web Applicationsfor Semantic Web Applications
• GREASE – Geographic Reasoning for Search Engines
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Presentation Structure
Conceptual Design of GKBKnowledge IntegrationUsing Geographic Knowledge in GKBGKB as an OntologyStatistics of the Ontologies CreatedApplications using GKBFinal Remarks
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Information Sources used by GKB
• Geo-Administrative and Geo-Physical Domain– Administrative– Postal– Gazetteers– Wikipedia
• Network Domain
– FCCN • Web domains• Web sites
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Architecture of GKB
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Feature concept in GKB
• A meaningful object in the selected domain of discourse [ISO19109].Ex.:
• countries, cities and localities
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Conceptual Design of GKB
• GKB meta-model
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Presentation Structure
Conceptual Design of GKBKnowledge IntegrationUsing Geographic Knowledge in GKBGKB as an OntologyStatistics of the Ontologies CreatedApplications using GKBFinal Remarks
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Knowledge Integration in GKB
• GKB hierarchy from different information sources• Algorithm:
– It searches the lowest common features types in both hierarchies
– If it holds, it identifies the common instances between the hierarchies
– Once the common instances are identified, it goes up the hierarchy and searches for the lowest common ancestor
– It verifies the distance (in number of relationships partOf) between the common instances of the features types and its ancestors. The ancestor, which has the small distance up to the common instances is merged through a relationship partOf with the ancestor in the another hierarchy.
The existing relationships in both hierarchies are maintained.
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Knowledge Integration in GKB
• GKB hierarchy from different information sources
H1
Norte
Grande Porto
Tâmega
MatosinhosVila
Nova de Gaia
Penafiel
NUT2
NUT3
MUNICIPALITYMUNICIPALITY
H2
Porto
MatosinhosVila
Nova de Gaia
Penafiel
DISTRITO
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Knowledge Integration in GKB
• GKB hierarchy from different information sources
H1
Norte
Grande Porto
Tâmega
MatosinhosVila
Nova de Gaia
Penafiel
NUT2
NUT3
MUNICIPALITYMUNICIPALITY
H2
Porto
MatosinhosVila
Nova de Gaia
Penafiel
DISTRITO
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Knowledge Integration in GKB
• GKB hierarchy from different information sources
H1
Norte
Grande Porto
Tâmega
MatosinhosVila
Nova de Gaia
Penafiel
NUT2
NUT3
MUNICIPALITYMUNICIPALITY
H2
Porto
MatosinhosVila
Nova de Gaia
Penafiel
DISTRITO
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Knowledge Integration in GKB
Merged Hierarchy
Norte
Grande Porto
Porto
Tâmega
PenafielMatosinhosVila
Nova de Gaia
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Presentation Structure
Conceptual Design of GKBKnowledge IntegrationUsing Geographic Knowledge in GKBGKB as an OntologyStatistics of the Ontologies CreatedApplications using GKBFinal Remarks
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– web site: www.cm-santiago-do-cacem.ptnetSiteSubDomain(33684,“www”).netSitePrefix(33684,“cm”).netSiteDomainToken(33684,“santiago-do-cacem”).netSiteTLD(33684,“pt”).
Using Geographic Knowledge in GKB
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• Rule-based assigned scopes by GKB to sites of Portugal
Site Type # of sites # of matches
distritos 33 17 (52%)
municipalities 288 261 (90%)
freguesias 300 124 (41%)
basic schools 1955 124 (6%)
training centers 152 55 (36%)
high schools 402 105 (26%)
Using Geographic Knowledge in GKB
• Scopes extended to the web pages under each one of the sites of matching subdomains
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Presentation Structure
Conceptual Design of GKBKnowledge IntegrationUsing Geographic Knowledge in GKBGKB as an OntologyStatistics of the Ontologies CreatedApplications using GKBFinal Remarks
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Statistics of the Ontologies Created
Statistic Portugal World
# of features 418,065 12,293
# of relationships 419,867 12,258
# of part-of relationships 418,340 (99.83%) 12,245 (99,89%)
# of equivalence relationships 395 (0.09%) 2,501(20,40%)
# of adjacency relationships 1,132 (0.27%) 13 (0.10%)
Avg. broader features per feature 1.0016 1.07
Avg. narrower features per feature 10.56 475.44
Avg. equivalent features per feature with equivalent 1.99 3.82
Avg. adjacent features per feature with adjacent 3.54 6.5
# of features without ancestors 3 (0.00%) 1(0.00%)
# of features without descendants 374,349 (89.54%) 12,045 (97,98%)
# of features without equivalent 417,867 (99.95%) 11,819 (96,14%)
# of features without adjacent 417,739 (99.92%) 12,291 (99,99%)
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Presentation Structure
Conceptual Design of GKBKnowledge IntegrationUsing Geographic Knowledge in GKBGKB as an OntologyStatistics of the Ontologies CreatedApplications using GKBFinal Remarks
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Applications using GKB
• NERC tool for recognizing geographical references in text
• Classification tool for assigning documents to a corresponding geographical scope
• Information retrieval interface for geographical queries
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Applications using GKB
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Final Remarks
• A domain-independent model for storing geographic and network knowledge
• Sharing of the collected knowledge as formal ontologies
• Geo-Net-PT01: The first public geographic ontology of Portugal - http://xldb.fc.ul.pt/geonetpt
• Future work– Augmenting the knowledge in GKB with geographic
entities extracted from the texts of the Portuguese Web