Mapping Policies to Web Rules: A Case of the KAoS Policy Language

Introduction And Motivation

Rules are among the most frequently used knowledge representation techniques. They can generally be categorized as reaction rules (event-condition-actions), integrity rules (rules of consistency checking), derivation rules (implicitly, derived rules), and production rules (Boley, Tabet, & Wagner, 2001). Facts can also be regarded as derivation rules with no premises.

Recently, rule markup languages have started to be considered as the vehicle for using rules on the Web and in other distributed systems, forming a new category of rule languages, referred to as Web rule languages (Wagner, Giurca, & Lukichev, 2006). The main strength of markup languages is their machine readability combined with their inherent potentials to easily be linked to distributed knowledge sources also represented in the form of markup languages (e.g., Data Type Definition (DTD), XML Schema (Fallside & Walmsley, 2004), Resource Description Framework (RDF) (Lassila & Swick, 1999), and Web Ontology Language (OWL) (Smith, Welty, & McGuinness, 2004)). Rule markup languages allow for the reuse, interchange, and publication of rules as well as their communication and execution in a distributed environment. More specifically, rule markup languages allow for specifying business rules as modular, standalone, units in a declarative way, and publishing and interchanging them between different systems and tools (Wagner, Giurca, & Lukichev, 2006). Rule markup languages play an important role in facilitating business-to-customer (B2C) and business-to-business (B2B) interactions over the Internet by enabling information exchange across various stakeholders. For example, they may be used to express derivation rules for enriching Web ontologies by adding definitions of derived concepts, or for defining data access permissions; to describe and publish the reactive behaviour of a system in the form of reaction rules; and to provide a complete XML-based specification of a software agent (Wagner, Giurca, & Lukichev, 2005).

RuleML and REWERSE Rule Markup Language (R2ML) are two newly and rapidly emerging Web rule languages that help with interchanging various types of rules from one rule domain to another. RuleML represents an initiative for creating a general rule markup language that will support different types of rules and different semantics (Boley, Tabet, & Wagner, 2001). R2ML more or less follows a similar idea to RuleML; however, its design follows Model Driven Architecture (MDA) defined by the Object Management Group (OMG) (Miller & Mukerji, 2003). Moreover, R2ML is a trial to cover a more comprehensive set of atoms and elements required to develop and define rules, thus bringing more flexibility to rule definition. These rule languages are designed to be conformant and compatible with the guidelines and use cases defined by the Rule Interchange Format (RIF) working group (Ginsberg, Hirtle, McCabe, & Patranjan, 2006).

Policies in the domain of autonomous computing are guiding plans that restrict the behaviour of autonomous agents in accessing the resources (Toninelli, Bradshaw, Kagal, & Montanari, 2005). The main advantage in using policies is the possibility to dynamically change the behaviour of the system by adjusting the policies without interfering with the internal code of the system. They can authorize/oblige the users to, or prohibit/dispense them from, accessing the resources or taking particular actions in the system. Policy languages can be considered as instructional sets that enable phrasing and putting systematic guidelines in place for a target agent. KAoS (Uszok, et al., 2003) is one of the most known policy languages that goes beyond the traditional policy systems by giving special care to the context to which the policies are applied. This is done by enabling these policy languages to use domain knowledge (a.k.a, vocabularies) readily available on the Internet and represented in knowledge representation markup languages such as XML-Schemas, RDF, and OWL.