Merging Deductive and Abductive Knowledge Bases: An Argumentation Context Approach

 

Juan Carlos Nieves and Helena Lindgren

 

The authors are with the Department of Computing Science, Umea University, SE-901 87, Umea, Sweden (e-mail: jcnieves@cs.umu.se, helena@cs.umu.se).

 

Manuscript received on August 1, 2013.
Accepted for publication on September 30, 2013.

 

Abstract

The consideration of heterogenous knowledge sources for supporting decision making is key to accomplish informed decisions, e.g., about medical diagnosis. Consequently, merging different data from different knowledge bases is a key issue for providing support for decision-making. In this paper, we explore an argumentation context approach, which follows how medical professionals typically reason, in order to merge two basic kinds of reasoning approaches based on logic programs: deductive and abductive inferences. In this setting, we introduce two kinds of argumentation frameworks: deductive argumentation frameworks and abductive argumentation frameworks. For merging these argumentation frameworks, we follow an approach based on argumentation context systems. We illustrate the approach by considering two different declarative specifications of evidence-based medical knowledge into logic programs in order to support informed medical decisions.

Key words: Knowledge representation, deductive knowledge bases, abductive knowledge bases.

  

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Acknowledgment

This research is partly funded by VINNOVA (Sweden's innovation agency) and the Swedish Brain Power.

 

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