presentation charvat cerba
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Ontology as Knowledge Basefor Spatial Data Harmonization
Otakar Cerba, Karel Charvat
University of West Bohemia, Plzen, Czech RepublicHelp Service Remote Sensing, Benesov, Czech Republic
26.06.2012 1INSPIRE 2012
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Objectives
Spatial data harmonization – basics Domain ontology – theory & essential principles Harmonization ontology – components Example of harmonization based on ontology Conclusion
26.06.2012 2INSPIRE 2012
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Spatial data harmonization
Activity for elimination or reduction of heterogeneities of various properties of spatial data to support interoperability
The elimination of the aspects of spatial data heterogeneity cannot be based on a creation of some uniform rules and data models, because, there are too many subjects with individual requirements – formats, precision, reference systems, terminology...
The harmonization processes should be divided into small and simple substeps
26.06.2012 3INSPIRE 2012
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Conditions of successful harmonization
Theoretical knowledge (domain, geomatic, IT...) Understandable user requirements Cooperation of experts Sequence of harmonization substeps Multi-level data description
26.06.2012 4INSPIRE 2012
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Why to harmonize
To enable a sharing, combining and publishing of data To re-use existing sources To improve data quality To use web services and other automatic tools (SaaS) To keep data interoperability (it's cool!) To increase the number of stakeholders To meet legislation requirements All reasons
are strongly
interconnected
26.06.2012 5INSPIRE 2012
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Ontology – Theory
To improve communication between all participating subjects (cartographers, users, IT experts, domain experts...)
...formal and formalized
explicit specification of
sharing conceptualizati
on … way how a human understands the world and how
it expresses
… exactly defined syntax… clearly
semantically defined concepts
… directly expressed
… precise list of terms
… suitable for re-use
26.06.2012 6INSPIRE 2012
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Ontology – Fundamental components
Class (Concept) – particular parts of domain structured by is-a relation
Individual – particular parts of domain that cannot be divided
Property – detail description of specifics of classes or individuals; object & data type properties
Axiom – logical constructs between elements of ontology (e.g. closure axiom, cover axiom)
Annotation – metadata, description, explanation
26.06.2012 7INSPIRE 2012
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Ontology: Classes & Properties
Classes Properties
26.06.2012 8INSPIRE 2012
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Role of ontology in harmonization process
HeterogeneousData
HarmonizationTool(s)
HarmonizedData
DataDescription
Knowledge& Experience
Rules &Methods Ontology
To formalize
and process extra
information26.06.2012 9INSPIRE 2012
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Data description in ontology
26.06.2012 10INSPIRE 2012
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Proposal of harmonization substeps
After reasoning
Before reasoning
26.06.2012 11INSPIRE 2012
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Inferred Ontology – Data Description
26.06.2012 12INSPIRE 2012
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LU/LC Legend mapping ontology
26.06.2012 13INSPIRE 2012
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LU/LC Legend mapping ontology – parameters
26.06.2012 14INSPIRE 2012
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LU/LC Legend mapping ontology – example
Reasoning
Asserted (original)information
Inferred (new) information
Equivalentclasses
26.06.2012 15INSPIRE 2012
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LU/LC Legend mapping ontology
26.06.2012 16INSPIRE 2012
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Harmonization in ETL tool
Input file(CLC)
Replicationto moreoutputs
Transformationto new data
models
Changingattributevalues
Outputs(PELCOM etc.)26.06.2012 17INSPIRE 2012
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Results of LULC data harmonization
CLC
PELCOM
PELCOMAfter manual final harmonization
26.06.2012 18INSPIRE 2012
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Conclusion
Harmonization is not only technical process but also semantic...
It is necessary to consider a suitability of data sets from the view of Data completeness Data quality (depend for purposes of result) Semantics of the data sets and classification systems
Ontologies enable knowledge transfer and better communication (including information sharing)
26.06.2012 19INSPIRE 2012