Abstract Policy rules are often written in organizations by a team of people in different roles and technical backgrounds. While user-generated content and community-driven ontologies become common practices in the semantic environments, machine-processable user-generated policies have been underexplored, and tool support for such policy acquisition is practically non-existent. We defined the concept and developed a tool for policy acquisition from the end users, grounded on the Semantic technologies. We describe a policy management environment (PME) for the Semantic Web and show its added value compared to existing policy-related developments. In particular, we detail a part of the PME, the policy acquisition tool that enables non-expert users to create and modify semantic policy rules. An empirical study has been conducted with 10 users, who were new to the semantic policy acquisition concept and the developed tool. The main task for the users was to model policies of two different scenarios using previously unknown to them ontologies. Overall, the users modeled successfully policies employing the tool, with minor deviations between their performance and feedback. Observation-based, quantitative and qualitative feedback on the concept and the implementation of the end-user policy acquisition tool is presented. 1

Web rule languages with the ability to cover various types of rules have been recently emerged to make interactions between web resources and broker agents possible. The chance of describing resources and users of a domain through the use of vocabularies is another feature of Web rule languages. Combination of these two properties makes Web rule languages an appropriate medium to make a hybrid model of representing both contexts and rules of a policy-aware system, such as a web service. In this paper, we describe how REWERSE Rule Markup Language (R2ML) can be employed to bridge between different policy languages using its rich set of rules, vocabulary, and built-in constructs. We show how the concepts of the KAoS and Rei policy languages can be transformed to R2ML and then from R2ML to the other policy languages. Following these mappings, we have implemented transformers, which enable us not only to share policies between KAoS and Rei, but also to transform policies onto other rule languages (e.g., F-Logic) for which transformations from/to R2ML are already developed. 1.