Version Control in Online Software Repositories

Software version control repositories provide a uniform and stable interface to manage documents and their version histories. Unfortunately, Open Source systems, for example, CVS, Subversion, and GNU Arch are not well suited to highly collaborative environments and fail to track semantic changes in repositories. We introduce document provenance as our Description Logic framework to track the semantic changes in software repositories and draw interesting results about their historic behaviour using a rule-based inference engine. To support the use of this framework, we have developed our own online collaborative tool, leveraging the fluency of the modern WikiWikiWeb

Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

Community-oriented software engineering ontology evolution

Software Engineering Ontology (SE Ontology) defines common shareable software engineering knowledge and typically provides software engineering concepts: what the concepts are, how they are related, and why they are related. These concepts facilitate common understanding of software engineering knowledge across multiple international software development sites. The SE Ontology is in machine understandable form to facilitate meaningful communication for remote social members. These social members use the SE Ontology but are not involved in the development process. Most existing ontologies including the SE Ontology are designed by individuals or small group of experts, not actual ontology users nor various groups of experts. It is effective if the ontology users can contribute in the process of creating and maintaining the ontologies they use. Social networking is becoming more prevalent enabling people to engage in remote collaboration to form goal-directed social networks. In this paper, we propose a social network based approach for ontology evolution for the SE Ontology. We analyze ontology evolution of the SE Ontology and propose the social network based approach for making ontology evolution more responsive to users? needs

The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page

Semantic wikis and the collaborative construction of ontologies: a case study

Ontologies are complex artifacts. They should seek consensus on the use of a set of modeled concepts. Some authors propose that these devices would be beneficial if they were built collaboratively. This article aims to address the use of a semantic wiki as an alternative to the collaborative construction of ontologies, and describes its ontological structure. Wikis are known as tools for collaborative construction of content. The semantic wiki is a research effort to integrate the concepts of wikis with the semantic web. The case study presented shows an implementation in Semantic MediaWiki: the best known and most used semantic wiki features by the academic community and the organizational environment

First Attempt towards a Standard Glossary of Ontology Engineering Terminology

In this paper we present the consensus reaching process followed within the NeOn consortium for the identification and definition of the activities involved in the ontology network development process. This work was conceived due to the lack of standardization in the Ontology Engineering terminology, which clearly contrasts with the Software Engineering field that boasts the IEEE Standard Glossary of Software Engineering Terminology. The paper also includes the NeOn Glossary of Activities, which is the result of the consensus reaching process here explained. Our future aim is to standardize the NeOn Glossary of Activities

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

prototypical implementations

In this technical report, we present prototypical implementations of innovative tools and methods developed according to the working plan outlined in Technical Report TR-B-09-05 [23]. We present an ontology modularization and integration framework and the SVoNt server, the server-side end of an SVN- based versioning system for ontologies in the Corporate Ontology Engineering pillar. For the Corporate Semantic Collaboration pillar, we present the prototypical implementation of a light-weight ontology editor for non-experts and an ontology based expert finder system. For the Corporate Semantic Search pillar, we present a prototype for algorithmic extraction of relations in folksonomies, a tool for trend detection using a semantic analyzer, a tool for automatic classification of web documents using Hidden Markov models, a personalized semantic recommender for multimedia content, and a semantic search assistant developed in co-operation with the Museumsportal Berlin. The prototypes complete the next milestone on the path to an integral Cor- porate Semantic Web architecture based on the three pillars Corporate Ontol- ogy Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in [23]