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

requirements and use cases

In this report, we introduce our initial vision of the Corporate Semantic Web as the next step in the broad field of Semantic Web research. We identify requirements of the corporate environment and gaps between current approaches to tackle problems facing ontology engineering, semantic collaboration, and semantic search. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. We propose an initial layout for an integrative architecture of a Corporate Semantic Web provided by these three core pillars

concept paper

In this concept paper, we outline our working plan for the next phase of the Corporate Semantic Web project. The plan covers the period from March 2009 to March 2010. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human- centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge. Each of these pillars will yield innovative methods and tools during the project runtime until 2013. We propose a concept draft and a working plan covering the next twelve months for an integrative architecture of a Corporate Semantic Web provided by these three core pillars

prototypical implementations ; working packages in project phase II

In this technical report, we present the concepts and first prototypical imple- mentations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge. The concepts and prototypes are based on the state of art analysis and identified requirements in the CSW report IV

state of the art analysis ; working packages in project phase II

In this report, we introduce our goals and present our requirement analysis for the second phase of the Corporate Semantic Web project. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge

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]

Validation and Evaluation

In this technical report, we present prototypical implementations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge, developed according to the working plan outlined in Technical Report TR-B-12-04. The prototypes complete the next milestone on the path to an integral Corporate Semantic Web architecture based on the three pillars Corporate Ontology Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search, as envisioned in TR-B-08-09

Strukturbasierte Partitionierung von Semantic Web Ontologien

Component-based development of large and complex software systems by small well defined building blocks improves the comprehension as well as the management and leads to reusable software modules and a scalable overall system. Accordingly, designing ontologies in a modular way is intuitively promising in order to benefit from the same advantages. However, the status quo is that the most publicly available ontologies are monolithic. For that reason the number as well as the size of available ontologies has increased with the growing utilization during the last years. In order to improve the efficient usage (e.g. through distributed and scoped reasoning for reasoners), to simplify the maintenance (e.g. through refactoring support) and to allow reusable components (e.g. through increased human understandability) there is a need to partition large ontologies into well-sized building blocks in a (semi-) automatic way. Especially from the viewpoint of the Semantic Web reusability is a crucial issue because an agreed common semantic model allows easy data integration and interoperability. Considering ontologies as networks of concepts connected through properties, utilizing network analysis techniques is a promising approach to analyze and partition ontologies. As a very well established discipline in science there are a lot of sophisticated methods, algorithms and tools for network analysis available. This work is driven by the belief that these methods can be modified and applied to ontologies, so that the ontology structure can be used to analyze the content and to identify regions, which can be seen as network "communities" representing subdomains of the ontology. Furthermore, the analysis of the structure enables a first evaluation of the usability by allowing different views, so that existing ontologies can be easier comprehended by ontology engineers. This is very important because refactoring and reusing existing models assume that these models are understood. In this regard, an adaptable structure-based ontology partitioning framework has been designed and implemented that utilizes community detection algorithms from the field of social network analysis. According to the motivation of the partitioning, the framework provides different configurable parameters. By this means the optimal solution for a certain motivation can be achieved. The proposed framework has been evaluated with a gold-standard approach for two concrete ontology partitioning cases. On the one hand, it was analyzed how term chunks from ontology documentation pages of thirteen ontologies can be reconstructed. On the other hand, it was investigated how the modules of four selected modular built ontologies can be reidentified. For both cases, 480 different combinations of configurations have been applied on each ontology. The performance of the framework has been measured with F-Measure similarity function applied on the reference model and the produced partitions. This resulted in very good as well as very bad results. For that reason, the next problem was to define a strategy to select the best configuration for the partitioning process based on the structure of the ontology and the motivation for partitioning. Two different approaches have been used in this regard. Firstly, the results with all ontologies and all configurations have been analyzed statistically. The values for the different parameters, which led to the best results, have been selected. Secondly, assuming that similar ontologies should be partitioned alike, each new ontology that should be partitioned has been compared to already partitioned ontologies with a distance metrics based on structural metrics. After the most similar ontology was identified, the configuration leading to the best results for the already known ontology has been applied on the new ontology. With both approaches similar tools could be outperformed significantly, whereas the similarity based approach led to minimally better results than the statistic approach. The overall result is that for both reconstructing term chunks as well as modular ontologies the reference models could be reproduced up to sixty percent. Even though this value is twice as good as the performance of the similar tools, this does not justify a fully automatic approach for ontology partitioning. However, it could be demonstrated that with the proposed framework at least a semi-automatic approach for ontology partitioning can be realized, that creates an acceptable first result that should be refined manually.Komponentenbasierte Entwicklung von komplexen Softwaresystemen verbessert die Wartbarkeit und führt zu wiederverwendbaren Softwaremodulen. Ausgehend von dieser Erfahrung wird angenommen, dass die komponentenbasierte Entwicklung von Ontologien ähnliche Vorteile bringt. Allerdings sind die meisten Ontologien monolithisch aufgebaut, so dass mit der steigenden Anzahl online verfügbarere Ontologien auch die Größe und Komplexität mit angestiegen ist. Für die effiziente Nutzung, die einfache Wartbarkeit und die Möglichkeit wiederverwendbarer Komponenten bedarf es daher geeigneter Partitionierungstechniken. Insbesondere im Kontext von Semantic Web ist die Wiederverwendung von Ontologien von essentieller Bedeutung, da diese die webübergreifende Datenintegration und Interoperabilität heterogener Systeme ermöglichen. In dieser Arbeit wird ein strukturbasierter Ansatz zu Partitionierung von Ontologien verfolgt, in dem Ontologien als Netzwerke repräsentiert werden. Diesen wird eine Kantengewichtung hinzugefügt, welches die semantischen Beziehungen innerhalb der Ontologien berücksichtig. Darauf aufbauend wird ein konfigurierbarer Ansatz zur Partitionierung von Ontologien mit Hilfe von Community Detection Algorithmen aus dem Bereich der sozialen Netzwerke erarbeitet. Dabei liegt das Hauptaugenmerk auf zwei konkreten Anwendungsfällen für die Partitionierung, nämlich der Modularisierung von existierenden komplexen Ontologien zur Vereinfachung der Wartbarkeit und der Erzeugung von Begriffsgruppierungen für die Dokumentationsseiten zur Unterstützung der Wiederverwendbarkeit. Anforderungen für beide Fälle werden aus existierenden Lösungen extrahiert, welche im späteren Prozess in einem Goldstandardansatz als Referenzmodell auch zur Evaluation verwendet werden. In experimentellen Analysen des vorgeschlagenen Ansatzes werden die besten Parameterwerte für die jeweiligen Anwendungsfälle ermittelt. Mit diesen wird das System dann mit den bereits existierenden Werkzeugen zur Ontologiepartitionierung SWOOP und Pato verglichen. In diesem direkten Vergleich kann gezeigt werden, dass der hier erarbeitete Ansatz signifikant bessere Ergebnisse als die beiden Konkurrenten liefern kann. Allerdings sind die Ergebnisse nicht so gut, dass davon ausgegangen werden kann, dass ein vollständisch automatischer Prozess für die Partitionierung möglich ist. Der strukturbasierte Ansatz zur Partitionierung kann nur für eine semiautomatische Partitionierung verwendet werden, so dass die Nutzer die Ergebnisse manuell nachbessern müssen

TurtleEditor: An ontology-aware web-editor for collaborative ontology development

Inspired by the shift of vocabulary development projects towards repository hosting services such as GitHub, we noticed the lack of ontology-aware editors that can be easily connected to these repositories. This motivated us to build a web client optimized for the communication with external repositories and including specific functionalities to ease the participation in collaborative ontology development efforts also for non-expert contributors. This paper describes TurtleEditor, an open-source web editor, which can load files from, and commit changes to a central repository and offers features such as syntax highlighting, syntax checking, auto-completion and a SPARQL endpoint to query the ontology