Ontology as Knowledge Base             for Spatial Data Harmonization                Otakar Cerba, Karel Charvat    Univer...
Objectives      Spatial data harmonization – basics      Domain ontology – theory & essential principles      Harmoniza...
Spatial data harmonization      Activity for elimination or reduction of     heterogeneities of various properties of spa...
Conditions of successful harmonization      Theoretical knowledge (domain, geomatic, IT...)      Understandable user req...
Why to harmonize      To enable a sharing, combining and publishing of     data      To re-use existing sources      To...
Ontology – Theory      To improve communication between all participating     subjects (cartographers, users, IT experts,...
Ontology – Fundamental components      Class (Concept) – particular parts of domain     structured by is-a relation     ...
Ontology: Classes & Properties             Classes                        Properties26.06.2012                   INSPIRE 2...
Role of ontology in harmonization process   Heterogeneous        Data        Data      Description                        ...
Data description in ontology26.06.2012              INSPIRE 2012        10
Proposal of harmonization substeps                                             BeforeAfter                                ...
Inferred Ontology – Data Description26.06.2012                  INSPIRE 2012            12
LU/LC Legend mapping ontology26.06.2012              INSPIRE 2012         13
LU/LC Legend mapping ontology – parameters26.06.2012           INSPIRE 2012               14
LU/LC Legend mapping ontology – example                                   Reasoning                                       ...
LU/LC Legend mapping ontology26.06.2012              INSPIRE 2012         16
Harmonization in ETL tool    Input file   Replication    Transformation   Changing       Outputs    (CLC)26.06.2012       ...
Results of LULC data harmonization                                                         PELCOMCLC26.06.2012            ...
Conclusion      Harmonization is not only technical process but also     semantic...      It is necessary to consider a ...
Thank you for your attention                   and questions                     cerba@kma.zcu.cz                      cha...
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Presentation charvat cerba

  1. 1. Ontology as Knowledge Base for Spatial Data Harmonization Otakar Cerba, Karel Charvat University of West Bohemia, Plzen, Czech Republic Help Service Remote Sensing, Benesov, Czech Republic26.06.2012 INSPIRE 2012 1
  2. 2. Objectives  Spatial data harmonization – basics  Domain ontology – theory & essential principles  Harmonization ontology – components  Example of harmonization based on ontology  Conclusion26.06.2012 INSPIRE 2012 2
  3. 3. 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 substeps26.06.2012 INSPIRE 2012 3
  4. 4. Conditions of successful harmonization  Theoretical knowledge (domain, geomatic, IT...)  Understandable user requirements  Cooperation of experts  Sequence of harmonization substeps  Multi-level data description26.06.2012 INSPIRE 2012 4
  5. 5. 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 (its cool!) All reasons  To increase the number of stakeholders are strongly  To meet legislation requirements interconnect ed26.06.2012 INSPIRE 2012 5
  6. 6. Ontology – Theory  To improve communication between all participating subjects (cartographers, users, IT experts, domain experts...) … exactly defined … clearly syntaxsemantically defined concepts ...formal and formalized explicit … precise list of … directly specification of terms expressed sharing conceptualization … suitable for re- … way how a human use understands the world26.06.2012 INSPIRE 2012 and how it expresses6
  7. 7. 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, explanation26.06.2012 INSPIRE 2012 7
  8. 8. Ontology: Classes & Properties Classes Properties26.06.2012 INSPIRE 2012 8
  9. 9. Role of ontology in harmonization process Heterogeneous Data Data Description Harmonization Harmonized Tool(s) Data Knowledge & Experience To formalize and Rules & process Ontology Methods extra26.06.2012 INSPIRE 2012 informatio 9 n
  10. 10. Data description in ontology26.06.2012 INSPIRE 2012 10
  11. 11. Proposal of harmonization substeps BeforeAfter reasoningreasoning26.06.2012 INSPIRE 2012 11
  12. 12. Inferred Ontology – Data Description26.06.2012 INSPIRE 2012 12
  13. 13. LU/LC Legend mapping ontology26.06.2012 INSPIRE 2012 13
  14. 14. LU/LC Legend mapping ontology – parameters26.06.2012 INSPIRE 2012 14
  15. 15. LU/LC Legend mapping ontology – example Reasoning Equivalent classes Inferred (new) information Asserted (original)26.06.2012 information INSPIRE 2012 15
  16. 16. LU/LC Legend mapping ontology26.06.2012 INSPIRE 2012 16
  17. 17. Harmonization in ETL tool Input file Replication Transformation Changing Outputs (CLC)26.06.2012 to more to new data INSPIRE 2012 attribute (PELCOM etc.) 17 outputs models values
  18. 18. Results of LULC data harmonization PELCOMCLC26.06.2012 INSPIREPELCOM 2012 18 After manual final harmonization
  19. 19. 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 INSPIRE 2012 19
  20. 20. Thank you for your attention and questions cerba@kma.zcu.cz charvat@ccss.cz26.06.2012 INSPIRE 2012 20
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