Representing Uncertainty in RuleML

Supporting Uncertainty and Inconsistency in Semantic Web Applications

Ensuring the consistency and completeness of Semantic Web ontologies is practically impossible, because of their scale and highly dynamic nature. Many web applications, therefore, must deal with vague, incomplete and even inconsistent knowledge. Rules were shown to be very effective in processing such knowledge, and future web services are expected to depend heavily on them. RuleML, which is th...

Uncertainty and RuleML Rulebases: A Preliminary Report

Uncertainty, like imprecision and vagueness, has gained considerable attention the last decade. To this extend we present a preliminary report on extending the Rule Markup Language (RuleML) with fuzzy set theory, in order to be able to represent and handle vague knowledge. We also provide semantics for the case of fuzzy FOL RuleML.

Representing E - Business Rules for the Semanti Web : Situated Courteous Logi Programs in RuleML

GeospatialRules: A Datalog+ RuleML Rulebase for Geospatial Reasoning

Representing and reasoning with qualitative geospatial relationships among regions is an important task in many geospatial applications. In this paper, we present a Datalog rulebase, GeospatialRules, which can be used for this task. The rulebase is built on top of the Region Connection Calculus (RCC). It includes rules, facts, and queries. The rules in GeospatialRules consist of a set of rules ...

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 b...