A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP
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 GRAPH...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/4/147 |
| _version_ | 1827745007844458496 |
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| author | Renaud Fabre Otmane Azeroual Joachim Schöpfel Patrice Bellot Daniel Egret |
| author_facet | Renaud Fabre Otmane Azeroual Joachim Schöpfel Patrice Bellot Daniel Egret |
| author_sort | Renaud Fabre |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:00:01Z |
| format | Article |
| id | doaj.art-604f0f9d79654cb6ae1d169ceafcf9a8 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-11T05:00:01Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-604f0f9d79654cb6ae1d169ceafcf9a82023-11-17T19:20:19ZengMDPI AGFuture Internet1999-59032023-04-0115414710.3390/fi15040147A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYPRenaud Fabre0Otmane Azeroual1Joachim Schöpfel2Patrice Bellot3Daniel Egret4Dionysian Economics Laboratory (LED), University of Paris 8, 93200 Saint-Denis, FranceGerman Centre for Higher Education Research and Science Studies (DZHW), 10117 Berlin, GermanyGERiiCO-Labor, Groupe d’Études et de Recherche Interdisciplinaire en Information et Communication, University of Lille, 59000 Lille, FranceAix Marseille University (AMU), CNRS, LIS, 13007 Marseille, FranceObservatoire de Paris, PSL University, 75006 Paris, FranceThe 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.https://www.mdpi.com/1999-5903/15/4/147assessor shiftgeometric graphweb usagelog pattern discoverypossibilistic graphical modelingscientific reasoning |
| spellingShingle | Renaud Fabre Otmane Azeroual Joachim Schöpfel Patrice Bellot Daniel Egret A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP Future Internet assessor shift geometric graph web usage log pattern discovery possibilistic graphical modeling scientific reasoning |
| title | A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP |
| title_full | A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP |
| title_fullStr | A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP |
| title_full_unstemmed | A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP |
| title_short | A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP |
| title_sort | multiverse graph to help scientific reasoning from web usage interpretable patterns of assessor shifts in graphyp |
| topic | assessor shift geometric graph web usage log pattern discovery possibilistic graphical modeling scientific reasoning |
| url | https://www.mdpi.com/1999-5903/15/4/147 |
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