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Ontology-Based Knowledge Management for Comprehensive Geriatric Assessment and Reminiscence Therapy on Social Robots

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Data Science for Healthcare

Abstract

MARIO is an assistive robot that has to support a set of knowledge-intensive tasks aimed at increasing autonomy and reducing loneliness in people with dementia and supporting caregivers in their activity to assess patients’ cognitive status. Examples of knowledge-intensive tasks are the comprehensive geriatric assessment (CGA) and the delivery of reminiscence therapy. In order to enable these tasks, MARIO features a set of abilities implemented by pluggable software applications. MARIO’s abilities contribute to and benefit from a common knowledge management framework. For example, the ability associated with the CGA retrieves questions to be posed to the patient from the framework and stores the obtained answers and associated relevant metadata. In this work we presents the MARIO knowledge management software framework, which combines robotics with ontology-based approaches and Semantic Web technologies. It consists of (1) a set of interconnected and modularized ontologies, meant to model all knowledge areas that are relevant for MARIO abilities, and (2) a set of software interfaces that provide high-level access to the ontology network and its associated knowledge base. Finally, we demonstrate how the knowledge management framework supports the applications for CGA and reminiscence therapy, implemented on top of the knowledge base.

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Acknowledgements

The research leading to these results has received funding from the European Union Horizon 2020—The Framework Programme for Research and Innovation (2014–2020) under grant agreement 643808 Project MARIO “Managing active and healthy aging with use of caring service robots.”

Author information

Authors and Affiliations

  1. University of Bologna, Bologna, Italy

    Luigi Asprino & Aldo Gangemi

  2. STLab, ISTC-CNR, Rome, Italy

    Luigi Asprino, Andrea Giovanni Nuzzolese, Valentina Presutti & Alessandro Russo

  3. University of Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

Authors
  1. Luigi Asprino
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  2. Aldo Gangemi
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  3. Andrea Giovanni Nuzzolese
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  4. Valentina Presutti
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  5. Diego Reforgiato Recupero
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  6. Alessandro Russo
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Corresponding author

Correspondence to Alessandro Russo .

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Editors and Affiliations

  1. Philips Research, Eindhoven, The Netherlands

    Sergio Consoli

  2. Dept of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

  3. Data Science Department, Philips Research, Eindhoven, The Netherlands

    Milan Petković

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Asprino, L., Gangemi, A., Nuzzolese, A.G., Presutti, V., Recupero, D.R., Russo, A. (2019). Ontology-Based Knowledge Management for Comprehensive Geriatric Assessment and Reminiscence Therapy on Social Robots. In: Consoli, S., Reforgiato Recupero, D., Petković, M. (eds) Data Science for Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-030-05249-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-05249-2_6

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