Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs
The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not curre...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/14/9/262 |
| _version_ | 1797488187418869760 |
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| author | Renaud Fabre Otmane Azeroual Patrice Bellot Joachim Schöpfel Daniel Egret |
| author_facet | Renaud Fabre Otmane Azeroual Patrice Bellot Joachim Schöpfel Daniel Egret |
| author_sort | Renaud Fabre |
| collection | DOAJ |
| description | The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes. |
| first_indexed | 2024-03-09T23:58:31Z |
| format | Article |
| id | doaj.art-8d0d8cf1bd2a4271a45787c8a66fd748 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T23:58:31Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-8d0d8cf1bd2a4271a45787c8a66fd7482023-11-23T16:20:45ZengMDPI AGFuture Internet1999-59032022-09-0114926210.3390/fi14090262Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge GraphsRenaud Fabre0Otmane Azeroual1Patrice Bellot2Joachim Schöpfel3Daniel Egret4Dionysian Economics Laboratory (LED), University of Paris 8, 93200 Saint-Denis, FranceGerman Centre for Higher Education Research and Science Studies (DZHW), 10117 Berlin, GermanyCNRS, LIS, Aix Marseille University (AMU), 13007 Marseille, FranceGERiiCO-Labor, Groupe d’Études et de Recherche Interdisciplinaire en Information et Communication, University of Lille, 59000 Lille, FranceObservatoire de Paris, Paris Sciences & Lettres University (PSL), 75006 Paris, FranceThe variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.https://www.mdpi.com/1999-5903/14/9/262community detectioncliquesgraph completiongraph subnetworkmodel interpretabilitymeta learning |
| spellingShingle | Renaud Fabre Otmane Azeroual Patrice Bellot Joachim Schöpfel Daniel Egret Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs Future Internet community detection cliques graph completion graph subnetwork model interpretability meta learning |
| title | Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs |
| title_full | Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs |
| title_fullStr | Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs |
| title_full_unstemmed | Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs |
| title_short | Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs |
| title_sort | retrieving adversarial cliques in cognitive communities a new conceptual framework for scientific knowledge graphs |
| topic | community detection cliques graph completion graph subnetwork model interpretability meta learning |
| url | https://www.mdpi.com/1999-5903/14/9/262 |
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