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How Much Do I Argue Like You? Towards a Metric on Weighted Argumentation Graphs

Author(s): Markus Brenneis, Maike Behrendt, Stefan Harmeling, Martin Mauve
Title: How Much Do I Argue Like You? Towards a Metric on Weighted Argumentation Graphs
Published: In Proceedings, September 2020
Keyword(s): argumentation graphs, online argumentation, metric
Abstract: When exchanging arguments with other people, it is interesting to know who of the others has the most similar opinion to oneself. In this paper, we suggest using weighted argumentation graphs that can model the relative importance of arguments and certainty of statements. We present a pseudometric to calculate the distance between two weighted argumentation graphs, which is useful for applications like recommender systems, consensus building, and finding representatives. We propose a list of desiderata which should be fulfilled by a metric for those applications and prove that our pseudometric fulfills these desiderata.
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