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A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

Author

Listed:
  • Renaud Fabre

    (Dionysian Economics Laboratory (LED), University of Paris 8, 93200 Saint-Denis, France)

  • Otmane Azeroual

    (German Centre for Higher Education Research and Science Studies (DZHW), 10117 Berlin, Germany)

  • Joachim Schöpfel

    (GERiiCO-Labor, Groupe d’Études et de Recherche Interdisciplinaire en Information et Communication, University of Lille, 59000 Lille, France)

  • Patrice Bellot

    (Aix Marseille University (AMU), CNRS, LIS, 13007 Marseille, France)

  • Daniel Egret

    (Observatoire de Paris, PSL University, 75006 Paris, France)

Abstract

The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or assessor shifts). Our Scientific Knowledge Graph GRAPHYP innovates with interpretable patterns of web usage, providing scientific reasoning with conceptual fingerprints and helping identify eligible hypotheses. In a previous article, we showed how usage log data, in the form of ‘documentary tracks’, help determine distinct cognitive communities (called adversarial cliques) within sub-graphs. A typology of these documentary tracks through a triplet of measurements from logs (intensity, variety and attention) describes the potential approaches to a (research) question. GRAPHYP assists interpretation as a classifier, with possibilistic graphical modeling. This paper shows what this approach can bring to scientific reasoning; it involves visualizing complete interpretable pathways, in a multi-hop assessor shift, which users can then explore toward the ‘best possible solution’—the one that is most consistent with their hypotheses. Applying the Leibnizian paradigm of scientific reasoning, GRAPHYP highlights infinitesimal learning pathways, as a ‘multiverse’ geometric graph in modeling possible search strategies answering research questions.

Suggested Citation

  • Renaud Fabre & Otmane Azeroual & Joachim Schöpfel & Patrice Bellot & Daniel Egret, 2023. "A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP," Future Internet, MDPI, vol. 15(4), pages 1-24, April.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:4:p:147-:d:1120190
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    References listed on IDEAS

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    1. Michael D. Cooper, 2001. "Usage patterns of a web‐based library catalog," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(2), pages 137-148.
    2. Lin, Yiling & Evans, James A. & Wu, Lingfei, 2022. "New directions in science emerge from disconnection and discord," Journal of Informetrics, Elsevier, vol. 16(1).
    3. Renaud Fabre & Otmane Azeroual & Patrice Bellot & Joachim Schöpfel & Daniel Egret, 2022. "Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs," Future Internet, MDPI, vol. 14(9), pages 1-18, September.
    4. Xusen Cheng & Xiao Lin & Xiao-Liang Shen & Alex Zarifis & Jian Mou, 2022. "The dark sides of AI," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 11-15, March.
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    1. Otmane Azeroual & Renaud Fabre & Uta Störl & Ruidong Qi, 2023. "Elastic Stack and GRAPHYP Knowledge Graph of Web Usage: A Win–Win Workflow for Semantic Interoperability in Decision Making," Future Internet, MDPI, vol. 15(6), pages 1-19, May.

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