Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

Abstract

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.

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

“…In that direction, it appears fruitful to start by collecting and modeling the search experience of researchers. As mentioned previously, this was our approach when we proposed GRAPHYP, a conceptual framework informed and modeled from the search experience of scholars who have explored the same research question [1]. From this descriptive conceptual framework, we now approach the process of the documentary choice of users/researchers among the available representations of data and documents.…”

“…In a previous article [1], we published a background of our methodology, GRAPHYP 1, hereafter called GR1, depicting documentary tracks of scientific literature in retrievable cliques of interpretable adversarial subgraphs. In what follows, with an extended methodology, hereafter called GR2, we seek to model the documentary choices made by previous users in order to detect assessor shifts between documentary tracks.…”

“…Our research question is thus as follows: How can we assist scientific reasoning with comprehensive representations of adversarial documentary tracks as a generator (see GR1 [1]), which can both be meaningful and usable to enable understanding in a discriminator (GR2)?…”

“…-"indirect": SKG GRAPHYP does not provide 'results', but a comprehensive representation of clickable maps of topologized results; -"intermediary": It does not deliver any final scientific assessment, but a methodology to reach the documentary set that seems to a scientist as being best adjusted to the hypotheses that are under review, as well as those that the user expects to simulate; -"neutral": Our typology of classification, not being referred to any assessment on the scientific content of captured documents, deals only with the information profile and content of user logs, formalized in a triplet of parameters measured from anonymized data (see [1] and Appendix A for more details): Intensity (How many readers? ), Variety (How many documents?)…”

“…How can we use these documentary tracks to assist the scientific reasoning of new users exploring the same research problem? These are questions that motivate the present work, in the continuation of a paper [1] dedicated to a new knowledge graph model, GRAPHYP, and its application to identify adversarial scientific communities. Our hypothesis is that a comprehensive modeling of web usage logs can make it possible to represent the main directions of answers that researchers have previously given to a research question.…”