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An Ontology-Based Decision Support Framework for Personalized Quality of Life Recommendations

  • Marina Riga
  • Efstratios Kontopoulos
  • Kostas Karatzas
  • Stefanos Vrochidis
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 313)

Abstract

As urban atmospheric conditions are tightly connected to citizens’ quality of life, the concept of efficient environmental decision support systems becomes highly relevant. However, the scale and heterogeneity of the involved data, together with the need for associating environmental information with physical reality, increase the complexity of the problem. In this work, we capitalize on the semantic expressiveness of ontologies to build a framework that uniformly covers all phases of the decision making process: from structuring and integration of data, to inference of new knowledge. We define a simplified ontology schema for representing the status of the environment and its impact on citizens’ health and actions. We also implement a novel ontology- and rule-based reasoning mechanism for generating personalized recommendations, capable of treating differently individuals with diverse levels of vulnerability under poor air quality conditions. The overall framework is easily adaptable to new sources and needs.

Keywords

Personalized decision support Ontology OWL 2 SPIN Air quality recommendations User profiling 

Notes

Acknowledgments

This work is partially funded by the European Commission under the contract number H2020-688363 hackAIR.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CERTH-ITI, Information Technologies InstituteThessalonikiGreece
  2. 2.ISAG-EI Group, Department of Mechanical EngineeringAristotle University of ThessalonikiThessalonikiGreece

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