Creating and Using Geospatial Ontology Time Series in a Semantic Cultural Heritage Portal

  • Tomi Kauppinen
  • Jari Väätäinen
  • Eero Hyvönen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5021)


Content annotations in semantic cultural heritage portals commonly make spatiotemporal references to historical regions and places using names whose meanings are different in different times. For example, historical administrational regions such as countries, municipalities, and cities have been renamed, merged together, split into parts, and annexed or moved to and from other regions. Even if the names of the regions remain the same (e.g., “Germany”), the underlying regions and their relationships to other regions may change (e.g., the regional borders of “Germany” at different times). As a result, representing and finding the right ontological meanings for historical geographical names on the semantic web creates severe problems both when annotating contents and during information retrieval. This paper presents a model for representing the meaning of changing geospatial resources. Our aim is to enable precise annotation with temporal geospatial resources and to enable semantic search and browsing using related names from other historical time periods. A simple model and metadata schema is presented for representing and maintaining geospatial changes from which an explicit time series of temporal part-of ontologies can be created automatically. The model has been applied successfully to represent the complete change history of municipalities in Finland during 1865–2007. The resulting ontology time series is used in the semantic cultural heritage portal CultureSampo to support faceted semantic search of contents and to visualize historical regions on overlaying maps originating from different historical eras.


Temporal Part Ontology Evolution Current Place Semantic Search Polygonal Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tomi Kauppinen
    • 1
  • Jari Väätäinen
    • 2
  • Eero Hyvönen
    • 1
  1. 1.Semantic Computing Research Group (SeCo)Helsinki University of Technology, and University of HelsinkiFinland
  2. 2.Geological Survey of Finland 

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