Exploiting Ontological Reasoning in Argumentation Based Multi-agent Collaborative Classification

Title: Exploiting Ontological Reasoning in Argumentation Based Multi-agent Collaborative Classification
Author: Zhiyong Hao, Bin Liu, Junfeng Wu, Jinhao Yao
Abstract: Argumentation-based multip-agent collaborative classification is a promisibng paradigm agreements in distributed environments. In this paper, we advance the research by introducing a new domain ontology enriched inductive learning approach for collaborative classification, in which agents are able to constructing argument taking into account their own domain knowledge. This paper focuses on classification rules inductive learning, and presents Arguing SATE-Prism, a domain ontology enriched approach for multi agent collaborative classification based on argumentation. Domain ontology, in this context, is exploited for driving a paradigm shift from traditional data centered hidden pattern mining to domain-driven actionable knowledge discovery. Preliminary experimental result show that higher classification accuracy can be achieved by exploiting ontological reasoning in argumentation based multi-agent collaborative classification. Our experiments also demonstrate that the proposed approach out-performs comparable classification paradigms in presence of instances with missing values, harnessing the advantages offered by ontological reasoning.
Keywords: Argumentation, Prism algorithm, Collaborative classification, Domain ontology
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Conference: Intelligent Information and Database Systems