Ontology as Knowledge Base for Spatial Data Harmonization

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Amos Paul
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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 Republic 1

2 Objectives Spatial data harmonization basics Domain ontology theory & essential principles Harmonization ontology components Example of harmonization based on ontology Conclusion 2

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 substeps 3

4 Conditions of successful harmonization Theoretical knowledge (domain, geomatic, IT...) Understandable user requirements Cooperation of experts Sequence of harmonization substeps Multi-level data description 4

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 (it's cool!) To increase the number of stakeholders To meet legislation requirements All reasons are strongly interconnect ed 5

6 Ontology Theory To improve communication between all participating subjects (cartographers, users, IT experts, domain experts...) clearly semantically defined concepts directly expressed suitable for re-use...formal and formalized explicit specification of sharing conceptualization exactly defined syntax precise list of terms way how a human understands the world and how it expresses 6

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, explanation 7

8 Ontology: Classes & Properties Classes Properties 8

9 Role of ontology in harmonization process Heterogeneous Data Data Description Knowledge & Experience Rules & Methods Harmonization Tool(s) Harmonized Data To formalize and process Ontology extra information 9

10 Data description in ontology 10

11 Proposal of harmonization substeps After reasoning Before reasoning 11

12 Inferred Ontology Data Description 12

13 LU/LC Legend mapping ontology 13

14 LU/LC Legend mapping ontology parameters 14

15 LU/LC Legend mapping ontology example Reasoning Equivalent classes Inferred (new) information Asserted (original) information 15

16 LU/LC Legend mapping ontology 16

17 Harmonization in ETL tool Input file Replication Transformation Changing Outputs (CLC) to more INSPIRE to new 2012 data attribute (PELCOM etc.) 17 outputs models values

18 Results of LULC data harmonization PELCOM CLC PELCOM 18 After manual final harmonization

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) 19

20 Thank you for your attention and questions 20

Uniform Resource Management

IST-Africa 2008 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds) IIMC International Information Management Corporation, 2008 ISBN: 978-1-905824-07-6 Uniform Resource Management Karel