Semantic rules for context-aware geographical information retrieval

  • Authors:

  • Carsten Keßler;Martin Raubal;Christoph Wosniok

  • Affiliations:

  • Institute for Geoinformatics, University of Münster, Germany;Department of Geography, University of California, Santa Barbara;Institute for Geoinformatics, University of Münster, Germany

  • Venue:
  • EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
  • Year:
  • 2009

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Abstract

Geographical information retrieval (GIR) can benefit from context information to adapt the results to a user's current situation and personal preferences. In this respect, semantics-based GIR is especially challenging because context information - such as collected from sensors - is often provided through numeric values, which need to be mapped to ontological representations based on nominal symbols. The Web Ontology Language (OWL) lacks mathematical processing capabilities that require free variables, so that even basic comparisons and distance calculations are not possible. Therefore, the context information cannot be interpreted with respect to the task and the current user's preferences. In this paper, we introduce an approach based on semantic rules that adds these processing capabilities to OWL ontologies. The task of recommending personalized surf spots based on user location and preferences serves as a case study to evaluate the capabilities of semantic rules for context-aware geographical information retrieval. We demonstrate how the Semantic Web Rule Language (SWRL) can be utilized to model user preferences and how execution of the rules successfully retrieves surf spots that match these preferences. While SWRL itself enables free variables, mathematical functions are added via built-ins - external libraries that are dynamically loaded during rule execution. Utilizing the same mechanism, we demonstrate how SWRL built-ins can query the Semantic Sensor Web to enable the consideration of real-time measurements and thus make geographical information retrieval truly context-aware.