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

Renaud Fabre, Otmane Azeroual (), Joachim Schöpfel, Patrice Bellot and Daniel Egret
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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

Future Internet, 2023, vol. 15, issue 4, 1-24

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.

Keywords: assessor shift; geometric graph; web usage; log pattern discovery; possibilistic graphical modeling; scientific reasoning; usability testing logs (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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