In collaborative agile ontology development projects support for modular reuse of ontologies from large existing remote repositories, ontology project life cycle management, and transitive dependency management are important needs. The Apache Maven approach has proven its success in distributed collaborative Software Engineering by its widespread adoption. The contribution of this paper is a new design artifact called OntoMaven. OntoMaven adopts the Maven-based development methodology and adapts its concepts to knowledge engineering for Maven-based ontology development and management of ontology artifacts in distributed ontology repositories.
There are many existing ontology engineering methodologies and ontology editors available. With its Maven-based approach for structuring the development phases into different goals providing different functionalities during the development project's life cycle, OntoMaven supports in particular agile ontology development methods, such as RapidOWL @cite_6 and COLM @cite_12 , as well as development methods which are inherently based on modularization such as aspect-oriented ontology development @cite_15 .
@cite_15 @cite_12 @cite_6
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Engineering phase specific tailored views on data are required to prevent over-engineering and to discover missing elements. This paper presents an approach to enhance a semantic Conceptual Data Model (CDM) to define the specific behavior of engineering phases. Therefore, a CDM gets enhanced by an ontology profile to specify required and non-admissible features for each engineering phase. This is exploited to enhance exis­ting transformations from Web Ontology Language (OWL) to Eclipse Modeling Language (EMF) to generate phase specific decorators representing the corresponding views on data items. The approach also supports engineering phase specific handling of constraints and rules that further augment the guidance of engineers through the system development process by applying knowledge management functions such as reasoning. As a result, the presented approach reveals a notably higher data quality and offers more analysis potential by enabling phase-specific definitions of views on data and specification of semantic checks on CDM level. Several use cases from aerospace engineering have been analyzed and the improvements in areas such as inconsistency detection, knowledge derivation, and guided system modeling are highlighted in this paper. With the emergence of tools for collaborative ontology engineering, more and more data about the creation process behind collaborative construction of ontologies is becoming available. Today, collaborative ontology engineering tools such as Collaborative Protege offer rich and structured logs of changes, thereby opening up new challenges and opportunities to study and analyze the creation of collaboratively constructed ontologies. While there exists a plethora of visualization tools for ontologies, they have primarily been built to visualize aspects of the final product the ontology and not the collaborative processes behind construction e.g. the changes made by contributors over time. To the best of the authors' knowledge, there exists no ontology visualization tool today that focuses primarily on visualizing the history behind collaboratively constructed ontologies. Since the ontology engineering processes can influence the quality of the final ontology, they believe that visualizing process data represents an important stepping-stone towards better understanding of managing the collaborative construction of ontologies in the future. In this application paper, the authors present a tool-PragmatiX-which taps into structured change logs provided by tools such as Collaborative Protege to visualize various pragmatic aspects of collaborative ontology engineering. The tool is aimed at managers and leaders of collaborative ontology engineering projects to help them in monitoring progress, in exploring issues and problems, and in tracking quality-related issues such as overrides and coordination among contributors. The paper makes the following contributions: i They present PragmatiX, a tool for visualizing the creation process behind collaboratively constructed ontologies ii the authors illustrate the functionality and generality of the tool by applying it to structured logs of changes of two large collaborative ontology-engineering projects and iii they conduct a heuristic evaluation of the tool with domain experts to uncover early design challenges and opportunities for improvement. Finally, the authors hope that this work sparks a new line of research on visualization tools for collaborative ontology engineering projects. Over the last decade, a globalization of the software industry took place, which facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the software engineering community, with not only components but also their problems and vulnerabilities being now shared. For example, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing these vulnerabilities at a global scale becomes an inherently difficult task since many of the existing resources required for such analysis still rely on proprietary knowledge representation. In this research, we introduce an ontology-based knowledge modeling approach that can eliminate such information silos. More specifically, we focus on linking security knowledge with other software knowledge to improve traceability and trust in software products (APIs). Our approach takes advantage of the Semantic Web and its reasoning services, to trace and assess the impact of security vulnerabilities across project boundaries. We present a case study, to illustrate the applicability and flexibility of our ontological modeling approach by tracing vulnerabilities across project and resource boundaries. Traditionally, evaluation methods in the field of semantic technologies have focused on the end result of ontology engineering efforts, mainly, on evaluating ontologies and their corresponding qualities and characteristics. This focus has led to the development of a whole arsenal of ontology-evaluation techniques that investigate the quality of ontologies as a product. In this paper, we aim to shed light on the process of ontology engineering construction by introducing and applying a set of measures to analyze hidden social dynamics. We argue that especially for ontologies which are constructed collaboratively, understanding the social processes that have led to their construction is critical not only in understanding but consequently also in evaluating the ontologies. With the work presented in this paper, we aim to expose the texture of collaborative ontology engineering processes that is otherwise left invisible. Using historical change-log data, we unveil qualitative differences and commonalities between different collaborative ontology engineering projects. Explaining and understanding these differences will help us to better comprehend the role and importance of social factors in collaborative ontology engineering projects. We hope that our analysis will spur a new line of evaluation techniques that view ontologies not as the static result of deliberations among domain experts, but as a dynamic, collaborative and iterative process that needs to be understood, evaluated and managed in itself. We believe that advances in this direction would help our community to expand the existing arsenal of ontology evaluation techniques towards more holistic approaches.