Git4Voc: Git-based Versioning for Collaborative Vocabulary Development

Collaborative vocabulary development in the context of data integration is the process of finding consensus between the experts of the different systems and domains. The complexity of this process is increased with the number of involved people, the variety of the systems to be integrated and the dynamics of their domain. In this paper we advocate that the realization of a powerful version control system is the heart of the problem. Driven by this idea and the success of Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences there are still important differences. These need to be considered within the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we were faced with during the creation of vocabularies collaboratively and discusses its distinction to software development. Based on these insights we propose Git4Voc which comprises guidelines how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs

VoCol Evaluation Material

VoCol Evaluation Materia

EffTE - Experiment Material

Different dependency graphs for test case

RDF Doctor: A Holistic Approach for Syntax Error Detection and Correction of RDF Data

Over the years, the demand for interoperability support between diverse applications has significantly in- creased. The Resource Definition Framework (RDF), among other solutions, is utilized as a data modeling language which allows for encoding the knowledge from various domains in a unified representation. More- over, a vast amount of data from heterogeneous data sources are continuously published in documents using the RDF format. Therefore, these RDF documents should be syntactically correct in order to enable software agents performing further processing. Albeit, a number of approaches have been proposed for ensuring error-free RDF documents, commonly they are not able to identify all syntax errors at once by failing on the first encountered error. In this paper, we tackle the problem of simultaneous error identification, and propose RDF-Doctor, a holistic approach for detecting and resolving syntactic errors in a semi-automatic fashion. First, we define a comprehensive list of errors that can be detected along with customized error messages to allow users for a better understanding of the actual errors. Next, a subset of syntactic errors is corrected automatically based on matching them with predefined error messages. Finally, for a particular number of errors, customized and meaningful messages are delivered to users to facilitate the manual corrections process. The results from empirical evaluations provide evidence that the presented approach is able to effectively detect a wide range of syntax errors and automatically correct a large subset of them

OpenBudgets.eu: A Distributed Open-Platform for Managing Heterogeneous Budget Data

OpenBudgets.eu (OBEU) provides an open-source software framework and accompanying Software-As-A-Service (SAAS) platform for supporting financial transparency, thus enhancing accountability within public sectors as well as influencing corruption prevention. To this end, a scalable framework for multi-stakeholders is developed, with the aim of maximizing flexibility and ease of use. The core features of the OBEU platform are: (1) A semantic data model used to integrate heterogenous budget data, giving a pre-defined structure to the input data; (2) a library of visualisation tools with a user-friendly interface, which enables stakeholders to visualise available data in different granularity and modality; (3) a library of data mining and comparative analysis tools, which enables the aggregation of existing data in order to obtain new outcomes and discover recent trends and patterns, and potentially forecasting budget measures; and (4) an interface for feedback and citizen engagement which enables users to evaluate, discuss and give feedback on the provided data. A demonstration of the OBEU platform and portal is available at http://apps.openbudgets.eu/ and can be easily embedded into municipalities’ websites

Distributed Vocabulary Development with Version Control Systems

Vocabularies are increasingly being developed on platforms for hosting version-controlled repositories, such as GitHub. However, these platforms lack important features that have proven useful in vocabulary development. We present VoCol, an integrated environment that supports the development of vocabularies using Version Con trol Systems. VoCol is based on a fundamental model of vocabulary development, consisting of the three core activities modeling, population, and testing. It uses a loose coupling of validation, querying, analytics, visualization, and documentation generation components on top of a standard Git repository. All components, including the version-controlled repository, can be configured and replaced with little effort to cater for various use cases

OpenBudgets.eu: A Distributed Open-Platform for Managing Heterogeneous Budget Data

<p>Poster in a RASH HTML format detailing the architecture of OpenBudgets platform.</p

EffTE: A Dependency-aware Approach for Test-Driven Ontology Development

The development of domain-specific ontologies requires joint efforts among different groups of stakeholders, such as ontology engineers and domain experts. By following a test-driven development technique, a set of test cases ensures that ontology changes do not violate predefined requirements. However, since the number of test cases can be large and their evaluation time may be high, the ontology development process can be negatively impacted. We propose EffTE, an approach for efficient test-driven ontology development relying on a graph-based model of dependencies between test cases. It enables prioritization and selection of test cases to be evaluated. Traversing the dependency graph is realized using breadth-first search along with a mechanism that tracks tabu test cases, i.e., test ca ses to be ignored for further evaluation due to faulty parent test cases. As a result, the number of evaluated test cases is minimized, thus reducing the time required for validating the ontology after each modification. We conducted an empirical evaluation to determine the efficiency of our approach. The evaluation results suggest that our approach is more efficient than an exhaustive evaluation of the test cases; in particular with an increasing ontology size and number of test cases. EffTE: A Dependency-aware Approach for Test-Driven Ontology Developmen