A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth

We illustrate a simple model of knowledge scaffolding, based on the process of building a corpus of knowledge, each item of which is linked to “previous” ones. The basic idea is that the relationships among the items of corpus can be essentially drawn as an acyclic network, in which topmost contribu...

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Main Authors: Franco Bagnoli, Guido de Bonfioli Cavalcabo’
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Future Internet
Subjects:
Online Access:https://www.mdpi.com/1999-5903/15/2/67
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author Franco Bagnoli
Guido de Bonfioli Cavalcabo’
author_facet Franco Bagnoli
Guido de Bonfioli Cavalcabo’
author_sort Franco Bagnoli
collection DOAJ
description We illustrate a simple model of knowledge scaffolding, based on the process of building a corpus of knowledge, each item of which is linked to “previous” ones. The basic idea is that the relationships among the items of corpus can be essentially drawn as an acyclic network, in which topmost contributions are “derived” from items at lower levels. When a new item is added to the corpus, we impose a limit to the maximum unit increase (i.e., “jumps”) of knowledge. We analyzed the time growth of the corpus (number of items) and the maximum knowledge, both showing a power law. Another result was that the number of “holes” in the knowledge corpus always remains limited. Our model can be used as a rough approximation to the asymptotic growth of Wikipedia, and indeed, actual data show a certain resemblance with our model. Assuming that the user base is growing, at beginning, in an exponential way, one can also recover the early phases of Wikipedia growth.
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spelling doaj.art-fcf220d49a814923aad1f5a7af31714a2023-11-16T20:37:50ZengMDPI AGFuture Internet1999-59032023-02-011526710.3390/fi15020067A Simple Model of Knowledge Scaffolding Applied to Wikipedia GrowthFranco Bagnoli0Guido de Bonfioli Cavalcabo’1Department of Physics and Astronomy and CSDC, University of Florence, Via G. Sansone 1, 50019 Sesto Fiorentino, ItalyDepartment of Physics and Astronomy and CSDC, University of Florence, Via G. Sansone 1, 50019 Sesto Fiorentino, ItalyWe illustrate a simple model of knowledge scaffolding, based on the process of building a corpus of knowledge, each item of which is linked to “previous” ones. The basic idea is that the relationships among the items of corpus can be essentially drawn as an acyclic network, in which topmost contributions are “derived” from items at lower levels. When a new item is added to the corpus, we impose a limit to the maximum unit increase (i.e., “jumps”) of knowledge. We analyzed the time growth of the corpus (number of items) and the maximum knowledge, both showing a power law. Another result was that the number of “holes” in the knowledge corpus always remains limited. Our model can be used as a rough approximation to the asymptotic growth of Wikipedia, and indeed, actual data show a certain resemblance with our model. Assuming that the user base is growing, at beginning, in an exponential way, one can also recover the early phases of Wikipedia growth.https://www.mdpi.com/1999-5903/15/2/67Wikipediaknowledge organizationscaffolding modelknowledge modellingknowledge visualizationknowledge graph
spellingShingle Franco Bagnoli
Guido de Bonfioli Cavalcabo’
A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth
Future Internet
Wikipedia
knowledge organization
scaffolding model
knowledge modelling
knowledge visualization
knowledge graph
title A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth
title_full A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth
title_fullStr A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth
title_full_unstemmed A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth
title_short A Simple Model of Knowledge Scaffolding Applied to Wikipedia Growth
title_sort simple model of knowledge scaffolding applied to wikipedia growth
topic Wikipedia
knowledge organization
scaffolding model
knowledge modelling
knowledge visualization
knowledge graph
url https://www.mdpi.com/1999-5903/15/2/67
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