Visualizing Defeasible Logic Rules for the Semantic Web Efstratios Kontopoulos 1 , Nick Bassiliades 1 , Grigoris Antoniou 2 1 Department of Informatics, Aristotle University of Thessaloniki GR-54124 Thessaloniki, Greece {nbassili, skontopo}@csd.auth.gr 2 Institute of Computer Science, FO.R.T.H. P.O. Box 1385, GR-71110, Heraklion, Greece antoniou@ics.forth.gr Abstract. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Such reasoning is useful in many Semantic Web applications, like policies, business rules, brokering, bargaining and agent negotiations. Nevertheless, defeasible logic is based on solid mathe- matical formulations and is, thus, not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclu- sions. Directed graphs can assist in confronting this drawback. They are a pow- erful and flexible tool of information visualization, offering a convenient and comprehensible way of representing relationships between entities. Their appli- cability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the arcs in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantage, by defining two distinct node types, for rules and atomic formulas, and four distinct connection types for each rule type in defea- sible logic and for superiority relationships. The paper also briefly presents a tool that implements this representation methodology. 1. Introduction Defeasible reasoning [22], a member of the non-monotonic reasoning family, consti- tutes a simple rule-based approach to reasoning with incomplete and conflicting in- formation. This approach offers two main advantages: (a) enhanced representational capabilities, allowing one to reason with incomplete and contradictory information, coupled with (b) low computational complexity compared to mainstream non- monotonic reasoning. Defeasible reasoning can represent facts, rules as well as priori- ties and conflicts among rules. Such conflicts arise, among others, from rules with ex- ceptions, which are a natural representation for policies and business rules [2]. And priority information is often available to resolve conflicts among rules. Potential ap- plications include security policies ([19]), business rules [1], e-contracting [15], per- sonalization, brokering [5], bargaining and agent negotiations ([14]).