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Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability

Author

Listed:
  • Basma Hamrouni

    (Computer Science Department, University of Ouargla, Ouargla 30000, Algeria
    These authors contributed equally to this work.)

  • Abdelhabib Bourouis

    (Research Laboratory on Computer Science’s Complex Systems, University of Oum El Bouaghi, Oum El Bouaghi 04000, Algeria
    These authors contributed equally to this work.)

  • Ahmed Korichi

    (Computer Science Department, University of Ouargla, Ouargla 30000, Algeria
    These authors contributed equally to this work.)

  • Mohsen Brahmi

    (Faculty FEM, University of Sfax, Sfax 3018, Tunisia
    These authors contributed equally to this work.)

Abstract

Background: Case-Based Reasoning (CBR) is a problem-solving paradigm that uses knowledge of relevant past experiences (cases) to interpret or solve new problems. CBR systems allow generating explanations easily, as they typically organize and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. An improvement of this paradigm is ontology-based CBR, an approach that combines, in the form of formal ontologies, case-specific knowledge with domain one in order to improve the effectiveness and explanation capability of the system. Intelligent systems make daily activities more easily, efficiently, and represent a real support for sustainable economic development. On the one hand, they improve efficiency, productivity, and quality, and, on the other hand, can reduce costs and cut waste. In this way, intelligent systems facilitate sustainable development, economic growth, societal progress, and improve efficiency. Aim: In this vision, the purpose of this paper is to propose a new generation of intelligent decision support systems for Business Model having the ability to provide explanations to increase confidence in proposed solutions. Findings/result: The performance results obtained show the benefits of the proposed solution with different requirements of an explanatory decision support system. Consequently, applying this paradigm for software tools of business model development will make a great promise for supporting business model design, sustainability, and innovation.

Suggested Citation

  • Basma Hamrouni & Abdelhabib Bourouis & Ahmed Korichi & Mohsen Brahmi, 2021. "Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability," Sustainability, MDPI, vol. 13(17), pages 1-28, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9819-:d:627233
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    3. Jiying Zhou & Mingwei Shao, 2023. "Evaluation of Green Innovation Efficiency in Chinese Provincial Regions under High-Quality Development and Its Influencing Factors: An Empirical Study Based on Hybrid Data Envelopment Analysis and Mul," Sustainability, MDPI, vol. 15(14), pages 1-31, July.
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    5. Naeimah Fahad S. Almawishir & Houcine Benlaria, 2023. "Using the PLS-SEM Model to Measure the Impact of the Knowledge Economy on Sustainable Development in the Al-Jouf Region of Saudi Arabia," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    6. Catalin Vrabie, 2023. "E-Government 3.0: An AI Model to Use for Enhanced Local Democracies," Sustainability, MDPI, vol. 15(12), pages 1-19, June.
    7. Xin Dai & Jie Tang & Qin Huang & Wenyue Cui, 2023. "Knowledge Spillover and Spatial Innovation Growth: Evidence from China’s Yangtze River Delta," Sustainability, MDPI, vol. 15(19), pages 1-26, September.

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