Author ORCID Identifier

https://orcid.org/ 0000-0002-2718-5426

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Publication Details

Proceedings of The 28th Irish Conference on Artificial Intelligence and Cognitive ScienceVolume: Vol-277

Abstract

Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy editors. This research contributes to the field of argumentation by employing a replicable modular design which is suitable for modelling reasoning under uncertainty applied to distinct real-world domains.

DOI

https://doi.org/10.21427/jb0g-bs68

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