Abstract. We introduce the OWL Plugin, a Semantic Web extension of the Protégé ontology development platform. The OWL Plugin can be used to edit ontologies in the Web Ontology Language (OWL), to access description logic reasoners, and to acquire instances for semantic markup. In many of these features, the OWL Plugin has created and facilitated new practices for building Semantic Web contents, often driven by the needs of and feedback from our users. Furthermore, Protégé’s flexible open-source platform means that it is easy to integrate customtailored components to build real-world applications. This document describes the architecture of the OWL Plugin, walks through its most important features, and discusses some of our design decisions. 1

The growing interest in the Semantic Web and the Web Ontology Language (OWL) will reveal the potential of Description Logics in industrial projects. The rich semantics of OWL provide powerful reasoning capabilities that help build, maintain and query domain models for many purposes.

In the last years, important steps have been undertaken to bring the e-learning web to its full potential.

Recent efforts towards the Semantic Web vision have lead to a number of standards such as OWL and Web Service languages. While these standards provide a technical infrastructure, software developers have little guidance on how to build real-world Semantic Web applications. Based on a realistic application scenario, we present some initial thoughts on a software architecture and a development methodology for Web services and agents for the Semantic Web. This architecture is driven by formal domain models (ontologies). The methodology applies best practices from agile development methodologies, including systematic tests, short feedback loops, and close involvement of domain experts. We illustrate how these techniques can be put into practice using the modern Semantic Web development tool Protégé, and indicate future possibilities. 1

This paper introduces an ontology-based framework to improve the preparation of ISO/IEC 27001 audits, and to strengthen the security state of the company respectively. Building on extensive previous work on security ontologies, we elaborate on how ISO/IEC 27001 artifacts can be integrated into this ontology. A basic introduction to security ontologies is given first. Specific examples show how certain ISO/IEC 27001 requirements are to be integrated into the ontology; moreover, our rule-based engine is used to query the knowledge base to check whether specific security requirements are fulfilled. The aim of this paper is to explain how security ontologies can be used for a tool to support the ISO/IEC 27001 certification, providing pivotal information for the preparation of audits and the creation and maintenance of security guidelines and policies. 1.

Abstract. Self-managing systems will be highly dependent upon information acquired from disparate applications, devices, components and subsystems. To be effectively managed, such information will need to conform to a common model. One standard that provides a common model for describing disparate computer and network information is the Common Information Model (CIM). Although CIM defines the models necessary for inferring properties about distributed systems, its specification as a semi-formal ontology limits its ability to support important requirements of a self-managing distributed system including knowledge interoperability and aggregation, as well as reasoning. To support these requirements, there is a need to model, represent and share CIM as a formal ontology. In this paper, we propose a framework for constructing a CIM ontology based upon previous research that identified mappings from Unified Modeling Language (UML) constructs to ontology language constructs. We extend and apply these mappings to a UML representation of the CIM Schema in order to derive a semantically valid and consistent formal CIM ontology. 1

Abstract — The application of business process execution and guidance to environments with highly dynamic situations and workflow diversity is hindered by rigid predefined workflow models. Software engineering environments constitute an acute example where developers could benefit from automated workflow guidance if the workflows were made sufficiently concrete and conformant to actual situations. A context-aware software engineering environment was developed utilizing semantic processing and situational method engineering to automatically adapt workflows utilizing an adaptive processaware information system. Workflows are constructed via context knowledge congruent to the current situation. Preliminary results suggest this technique can be beneficial in addressing high workflow diversity while providing useable guidance and reducing workflow modeling effort. Keywords- application of semantic processing; domainoriented semantic applications; automated workflow adaptation; situational method engineering; process-aware information systems; software engineering environments I.

In MultiAgent Based Simulation (MABS), the observation of simulation results is a complex task: interactions between simulation agents produce a large mass of simulation results, which is particularly complex to analyze. We are assured that the use of another second multiagent system is a suitable approach to achieve this task. This second system then introduces a new category of agents called observation agents. The first step to build this multiagent system for observation is to define a specification of concepts used between its observation agents. Our contribution is an ontology for observation of MABS. This ontology is composed of a set of concepts and relations usable to acquire and process simulation results, and to produce their presentation form. This ontology has three main root classes: ObservationElements, Processing, and Presentation.

processes and tools, concrete preventative and analytical software quality assurance activities are still typically manually triggered and determined, resulting in missed or untimely quality opportunities and increased project overhead. Quality goals, when defined, lack holistic environmental support for automated performance measurement and governance that is tightly integrated in the low-level operational software engineering processes. This results in higher quality risks and cost risks. Based on adaptive process management, an approach is presented that injects situationally-determined quality measure proposals into the concrete workflows of software engineers, using contextual semantic knowledge and multi-agent quality goal tracking and decision-making. Our evaluation shows the feasibility of the approach for automatically providing timely quality measure guidance to software engineers without disrupting their current activities. This supports process governance while reducing quality risks and costs during software development projects. Keywords-software quality assurance; process-centered software engineering; adaptive process management; semantic technology; agents; Goal-Question-Metric technique I.

A review of the literature on evaluating reasoning systems reveals that it is a very broad area with wide variation in depth and breadth of research on metrics and tests. Consolidation is hampered by nonstandard terminology, differing methodologies, scattered application domains, unpublished algorithmic details, and the effects of domain content and context on the choice of metric and tests. The field of information metrology, which applies to reasoning as a kind of information processing, is still emerging from ad hoc experience in evaluating narrow kinds of information systems. This report begins to bring order to the area by categorizing reasoning systems according to their capabilities. The characteristics of each category can be used as a basis for evaluating and testing reasoning systems claiming to be in that category. Capabilities are analyzed along several dimensions, including representation languages, inference, and user and software interfaces. The report groups representation languages by their relation to first-order logic, and model-theoretic properties, such as soundness and completeness. Inference procedures are divided into deduction, induction, abduction, and analogical reasoning. Capabilities of user and software interfaces are described as they apply to