Proof Explanation in the DR-DEVICE System

Trust is a vital feature for Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design and implementation of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension

Large-scale Parallel Stratified Defeasible Reasoning

We are recently experiencing an unprecedented explosion of available data from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts

Reason Maintenance - State of the Art

This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques

Representing Business Contracts in RuleML

This paper presents an approach for the specification and implementation of translating contracts from a human-oriented form into an executable representation for monitoring. This will be done in the setting of RuleML. The task of monitoring contract execution and performance requires a logical account of deontic and defeasible aspects of legal language; currently such aspects are not covered by RuleML; accordingly we show how to extend it to cover such notions. From its logical form, the contract will be thus transformed into a machine readable rule notation and eventually implemented as executable semantics via any mark-up languages depending on the client's preference, for contract monitoring purposes

A Paradox of Inferentialism

John McDowell articulated a radical criticism of normative inferentialism against Robert Brandom’s expressivist account of conceptual contents. One of his main concerns consists in vindicating a notion of intentionality that could not be reduced to the deontic relations that are established by discursive practitioners. Noticeably, large part of this discussion is focused on empirical knowledge and observational judgments. McDowell argues that there is no role for inference in the application of observational concepts, except the paradoxical one of justifying the content of an observational judgment in terms of itself. This paper examines the semantical consequences of the analysis of the content of empirical judgments in terms of their inferential role. These, it is suggested, are distinct from the epistemological paradoxes that McDowell charges the inferentialist approach with

ALE Defeasible Description Logic

One of Semantic Web strengths is the ability to address incomplete knowledge. However, at present, it cannot handle incomplete knowledge directly. Also, it cannot handle non-monotonic reasoning. In this paper, we extend ALE Defeasible Description Logic with existential quantifier, i.e., ALE Defeasible Description Logic. Also, we modify some parts of the logic, resulting in an increasing efficiency in its reasoning