By Tapani Sarjakoski, Maribel Yasmina Santos, L. Tiina Sarjakoski

This booklet collects leading edge study offered on the nineteenth convention of the organization of Geographic details Laboratories in Europe (AGILE) on Geographic info technology, held in Helsinki, Finland in 2016.

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Details of all notations, their semantics and interpretations refer to Baader et al. (2003). 1 Reasoning with Description Logics DLs can be used to describe the knowledge bases (KB) for a concrete domain. A knowledge base consists of a set of terminological axioms (TBox) and a set of assertional axioms (ABox). g. Axioms (1)–(5)), while an ABox describes the known facts about the world. An example ABox may look like: Inferring Complex Geographical Concepts with Implicit … 27 where a, b are data instances, and the ABox asserts that a is a building and b is a’s nearest neighborhood.

Inferring Complex Geographical Concepts with Implicit … 31 a peninsula needs not to be at the national scale. We add this constraint here only to demonstrate the formalism and the inference process. 1 Characterization of Peninsula Candidates The inference starts by parsing the OWL document of Peninsula using the API. e. Region) is needed for the subsequent reasoning. However, only ‘Austrilia_sea’ and ‘Austrilia_land’ regions are available in the database (Fig. Land test. Therefore, we generated the candidate bend regions using the algorithm described in Sect.

Finally, spatial computing libraries such as GDAL8 provide specific raster and vector views of contents. While they are powerful for complex data manipulations and format conversions, these tools tend to lock users into particular representational choices and burden them with implementation details. By contrast, our mission is to enable question-based spatial computing through information content and quality abstractions. These abstractions are provided by the core concepts of spatial information and the computations on them, as summarized in the next section.

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