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

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Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support (ICDSST 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 313))

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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.

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Notes

  1. 1.

    http://pescado-project.upf.edu/.

  2. 2.

    http://newsroom.ucla.edu/releases/new-app-lets-you-check-air-quality-as-easily-as-checking-the-weather.

  3. 3.

    http://www.greenpeace.org/india/Clean-Air-Nation/.

  4. 4.

    https://airvisual.com/app.

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Acknowledgments

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

Author information

Authors and Affiliations

  1. CERTH-ITI, Information Technologies Institute, Thessaloniki, Greece

    Marina Riga, Efstratios Kontopoulos, Stefanos Vrochidis & Ioannis Kompatsiaris

  2. ISAG-EI Group, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Marina Riga & Kostas Karatzas

Authors
  1. Marina Riga
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  2. Efstratios Kontopoulos
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  3. Kostas Karatzas
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  4. Stefanos Vrochidis
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  5. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Marina Riga .

Editor information

Editors and Affiliations

  1. SimTech Simulation Technology, Graz, Austria

    Fatima Dargam

  2. Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece

    Pavlos Delias

  3. University of Namur, Namur, Belgium

    Isabelle Linden

  4. ULB, Brussels, Belgium

    Bertrand Mareschal

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Riga, M., Kontopoulos, E., Karatzas, K., Vrochidis, S., Kompatsiaris, I. (2018). An Ontology-Based Decision Support Framework for Personalized Quality of Life Recommendations. In: Dargam, F., Delias, P., Linden, I., Mareschal, B. (eds) Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support. ICDSST 2018. Lecture Notes in Business Information Processing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-90315-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-90315-6_4

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  • Online ISBN: 978-3-319-90315-6

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