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Social Semantic Web Framework for Industrial Synergies Initiation

Author

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  • Mohamed Raouf Ghali
  • Jean‐Marc Frayret

Abstract

Industrial synergies join two or more organizations that initially functioned as independent economic actors—that may originate from different sectors—together in order to share resources and exchange by‐products for mutual environmental, financial, and social benefits for its participants. Industrial symbioses (ISs) are networks of industrial synergies that can be initiated and created over time in various manners. In practice, the initiation of an industrial synergy, and particularly the identification of by‐product compatibilities, relies on direct or facilitated knowledge and information sharing, which is essential for discovering industrial synergy opportunities. Beyond its potential contribution to facilitate knowledge and information sharing among organizations, the Social Semantic Web (SSW) also has the potential to facilitate the initiation of industrial synergy by systematically and automatically identifying and recommending by‐products exchange compatibilities to potential partners. This framework exploits the ability of the sematic web to enable the search for analogies between potential partners within a region or district and existing industrial synergies around the world. This paper proposes the Social Semantic Web for Industrial Synergies Initiation (SSWISI) framework for the initiation of industrial synergies, which is based on the Social Semantic Web. The framework proposed in this paper adopts the concept of Linked Open Data (LOD), which enables the sharing and exchanging of information with external systems. This feature distinguishes the proposed framework from the existing approaches in its initiation of industrial synergies.

Suggested Citation

  • Mohamed Raouf Ghali & Jean‐Marc Frayret, 2019. "Social Semantic Web Framework for Industrial Synergies Initiation," Journal of Industrial Ecology, Yale University, vol. 23(3), pages 726-738, June.
  • Handle: RePEc:bla:inecol:v:23:y:2019:i:3:p:726-738
    DOI: 10.1111/jiec.12814
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    Cited by:

    1. Agneta Ghose & Matteo Lissandrini & Emil Riis Hansen & Bo Pedersen Weidema, 2022. "A core ontology for modeling life cycle sustainability assessment on the Semantic Web," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 731-747, June.
    2. Sergio Barile & Clara Bassano & Raffaele D’Amore & Paolo Piciocchi & Marialuisa Saviano & Pietro Vito, 2021. "Insights of Digital Transformation Processes in Industrial Symbiosis from the Viable Systems Approach ( vSa )," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    3. Chris Davis & Graham Aid, 2022. "Machine learning‐assisted industrial symbiosis: Testing the ability of word vectors to estimate similarity for material substitutions," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 27-43, February.

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