A review of argumentation for the Social Semantic Web

Schneider, Jodi; Groza, Tudor; Passant, Alexandre

doi:10.3233/sw-2012-0073

Cited by 71 publications

(32 citation statements)

References 137 publications

(198 reference statements)

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“…As discussed earlier, studies in computational argumentation or argument mining gained attention among researchers very recently. Arguments from various types of texts such as formal legal texts [16,17], essays [18,19], social semantic web [20], etc. have been attempted.…”

Section: Related Workmentioning

confidence: 99%

Changing views: Persuasion modeling and argument extraction from online discussions

Dutta

Das

Chakraborty

2020

Information Processing & Management

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Persuasion and argumentation are possibly among the most complex examples of the interplay between multiple human subjects. With the advent of the Internet, online forums provide wide platforms for people to share their opinions and reasonings around various diverse topics. In this work, we attempt to model persuasive interaction between users on Reddit, a popular online discussion forum. We propose a deep LSTM model to classify whether a conversation leads to a successful persuasion or not, and use this model to predict whether a certain chain of arguments can lead to persuasion. While learning persuasion dynamics, our model tends to identify argument facets implicitly, using an attention mechanism. We also propose a semi-supervised approach to extract argumentative components from discussion threads. Both these models provide useful insight into how people engage in argumentation on online discussion forums.

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Section: Related Workmentioning

confidence: 99%

Changing views: Persuasion modeling and argument extraction from online discussions

Dutta

Das

Chakraborty

2020

Information Processing & Management

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“…In [25] the authors conclude that collectives in Wikipedia follow their self-imposed rules regarding well defined and formalized processes, such as featured articles. Schneider et al [26,27,28,29] discussed multiple different aspects and the importance of consensus finding on Wikipedia and the Social Semantic Web, by analyzing the history of articles in said systems, further strengthening the need for tools and analyses to be able to better understand and support digital collaborative endeavors.…”

Section: Collaborative Authoring Systemsmentioning

confidence: 99%

How to apply Markov chains for modeling sequential edit patterns in collaborative ontology-engineering projects

Walk

Singer

Strohmaier

et al. 2015

International Journal of Human-Computer Studies

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With the growing popularity of large-scale collaborative ontologyengineering projects, such as the creation of the 11 th revision of the International Classification of Diseases, we need new methods and insights to help project-and community-managers to cope with the constantly growing complexity of such projects. In this paper, we present a novel application of Markov chains to model sequential usage patterns that can be found in the change-logs of collaborative ontology-engineering projects. We provide a detailed presentation of the analysis process, describing all the required steps that are necessary to apply and determine the best fitting Markov chain model. Amongst others, the model and results allow us to identify structural properties and regularities as well as predict future actions based on usage sequences. We are specifically interested in determining the appropriate Markov chain orders which postulate on how many previous actions future ones depend on. To demonstrate the practical usefulness of the extracted Markov chains we conduct sequential pattern analyses on a large-scale collaborative ontology-engineering dataset, the International Classification of Diseases in its 11 th revision. To further expand on the usefulness of the presented analysis, we show that the collected sequential patterns provide potentially actionable information for user-interface designers, ontology-engineering tool developers and project-managers to monitor, coordinate and dynamically adapt to the natural development processes that occur when collaboratively engineering an ontology. We hope that presented work will spur a new line of ontology-development tools, evaluation-techniques and new insights, further taking the interactive nature of the collaborative ontology-engineering process into consideration.

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“…Actuellement, tous les candidats sont considérés et peuvent être acceptés, même s'ils sont en conflit. Pour sélectionner les candidats qui ne sont pas en conflits, il serait possible d'utiliser la théorie de l'argumentation (Schneider et al, 2013) associée au score de confiance. Une autre perspective est d'intégrer une valeur de qualité de chaque source à l'intérieur de la fonction de confiance.…”

Section: Conclusion Et Perspectivesunclassified

Construction d’une ontologie par transformation de systèmes d’organisation des connaissances et évaluation de la confiance

Amarger¹,

Chanet²,

Haemmerlé³

et al. 2015

isi

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Cet article présente une méthode originale de création d'une base de connaissances (ontologie) à partir de transformations de plusieurs sources de connaissances. Ces sources sont issues des systèmes d'organisation des connaissances (thésaurus, taxonomies, etc.). Les sources sont pondérées par un score de confiance. La méthode s'appuie sur un module défini à l'aide de patrons de conception ontologiques. Nous avons mis en oeuvre cette méthode dans un contexte agronomique. Nous proposons dans cet article plusieurs fonctions de pondération que nous avons évaluées à partir de trois sources : Agrovoc, TaxRef et NCBI. ABSTRACT. This paper presents an original method of knowledge base (ontology) creation from transformations of several sources of knowledge. These sources are derived from the knowledge organization systems (thesauri, taxonomies, etc.). The sources are weighted by a confidence score. The method is based on a module aggregating ontological design patterns. We have implemented this method in an agronomical context. We propose in this paper several weighted functions that we have evaluated from three sources: Agrovoc, TaxRef and NCBI.

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A review of argumentation for the Social Semantic Web

Cited by 71 publications

References 137 publications

Changing views: Persuasion modeling and argument extraction from online discussions

Changing views: Persuasion modeling and argument extraction from online discussions

How to apply Markov chains for modeling sequential edit patterns in collaborative ontology-engineering projects

Construction d’une ontologie par transformation de systèmes d’organisation des connaissances et évaluation de la confiance

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