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Der Einsatz von Ontologien wird bereits in vielen Anwendungsbereichen als Werkzeug für die Strukturierung und die Verbesserung der Zugänglichkeit von Informationen unterschiedlichster Art genutzt. Sie ermöglichen die explizite Formulierung der Bedeutung von Konzepten und Strukturen beliebiger Domänen. Auch im Rahmen der Zusammenarbeit in und zwischen Gruppen ist der Austausch und die Verarbeitung von Informationen für den Verlauf und den Erfolg der Kooperation von erheblicher Bedeutung. Daher liegt es nahe, auch kollaborative Aktivitäten durch den Einsatz von Ontologien zu unterstützen. Aktuelle Arbeiten in diesem Themenbereich fokussieren jedoch meist auf ausgewählte Aspekte der Zusammenarbeit wie etwa der Kommunikation zwischen den Gruppenmitgliedern oder die Unterstützung durch eine konkrete Softwarekomponente. In dieser Arbeit wird dagegen von einer ganzheitlichen Betrachtung von Kooperationssituationen ausgegangen. Dabei werden die an einer Kooperation beteiligten Personen und die eingesetzten technischen Komponenten als ein gesamtes soziotechnisches System betrachtet, dessen Elemente nicht losgelöst voneinander betrachtet werden können. Das Ziel dieser Arbeit besteht einerseits darin, zu untersuchen, wie sich der Einsatz von Ontologien auf die Unterstützung der Zusammenarbeit auswirkt und andererseits, welche Möglichkeiten sich hieraus für die Gestaltung von Kooperationssystemen ableiten lassen. Einige dieser Möglichkeiten werden im praktischen Teil prototypisch implementiert, um die technische und wirtschaftliche Umsetzbarkeit zu evaluieren.
The publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.
This paper is grounded in the emerging field of web science and shall contribute to its further classification and demarcation by illustrating the current state of »web-native research methods«. It builds upon an initial arraying work of Richard Rogers, who coined the term »Digital Methods« for research with methods that were »born« in the web, and illustrated and organized them in his eponymous book in 2013. This paper attempts to develop a more appropriate illustration of the Digital Methods by following the web’s very own, hypertextual, network-like nature, in particular by construing an ontological representation on the base of the Web Ontology Language (OWL). By virtue of decomposing the book into granular information units and their subsequent reassembly into OWL entities, immediate access to the entire knowledge domain can be provided, and coherencies, interrelations and distinctions between concepts become apparent. The ontology’s structure was induced narrowly along the provided examples of research projects and subsequently clustered in topic groups, of which the three most important ones were (a) the Digital Methods as an arraying space of web-native methodology, (b) a collection of concrete applications of these Digital Methods in research projects, and (c) a hierarchical scheme of traditional sciences with a distinct interest in answering research questions with help of Digital Methods. Subsequently, the ontology was evaluated in three general dimensions: Deriving user stories and scenarios provided means to validate the utilization quality; the accuracy and reliability of the resulting structure was validated with help of a control group of web-native research projects; and process control instruments served as a validator for the ontology’s correctness. Despite the ontology itself, this paper also resulted in a first interpretation of the produced information: Statements about research practise in social science, politics and philosophy were as possible as findings about commonly applied varieties of methods. Concluding, the present paper proposes a process of ontology engineering, an evaluation of the ontology’s value, and an interpretation of the ontology’s content.
The amount of data produced and stored in multiple types of distributed data sources is growing steadily. A crucial factor that determines whether data can be analyzed efficiently is the use of adequate visualizations. Almost simultaneously with the ongoing availability of data numerous types of visualization techniques have emerged. Since ordinary business intelligence users typically lack expert visualization knowledge, the selection and creation of visualizations can be a very time- and knowledge-consuming task. To encounter these problems an architecture that aims at supporting ordinary BI users in the selection of adequate visualizations is developed in this thesis. The basic idea is to automatically provide visualization recommendations based on the concrete BI scenario and formalized visualization knowledge. Ontologies that formalize all relevant knowledge play an important role in the developed architecture and are the key to make the knowledge machine-processable.