Classifying phenomena is a key step to building new knowledge, especially in the early stages of a research process. It can bring about multiple advantages and insights, such as overview and comparison. Yet it also poses several risks and constraints. Thankfully, challenges can be over-come by re-classifying items in a domain with alternative classification principles, which lead to new insights or perspectives, as well as highlight previously neglected considerations. This process can be supported by graphic representations. Visualizing the drawn (and redrawn) distinctions can make a classification accessible and versatile, which makes it easier to compare with other classifications. Visualizing classifications can augment the entire research process, including hypothesis formation, testing, interpretation and result reporting. There is no systematic overview of methods to represent (especially qualitative) classifications graphically. This paper fills that gap in the literature. We distinguish between four types of visual classifications, based on their differing ability to emphasize hierarchies or group relations. We label these four types as compilations, configurations, layers, and trees. We analyze their benefits for the research process and point out potential risks to consider when using visualization for classifications purposes in social science research.
Wikis have their seeds in the easy collaborative editing and maintenance of web pages. This was picked up by tremendously successful public projects such as the online encyclopedia Wikipedia. Creating, modifying and maintaining of wiki articles implies social structures and dependencies between wiki authors and wiki articles themselves. The general challenge of this work is to consider these structures as dynamic evolving networks and to point out prominent behaviors in large wiki-based networks. We present an environment capable of handling data management, measurement and visualization issues for the dynamic network analysis of publicly available wiki data.
This paper introduces a new model of personalized usage of the internet that is based on technologies of user representation, artificial intelligence and semantic augmentation of the content. By taking advantage of internet’s unprecedented dynamics, compared to traditional media, this user representation model incorporates cognitive, mainly, psychology theories, combined with parameters that constitute more traditional approaches in user profiling (such as demographics, expertise, etc). The purpose of this research is to alleviate difficulties that massive approaches impose on areas such as education and information processing, by integrating intelligent adaptive characteristics into web applications; this can lead to a highly adapted to each user’s needs content and more effective, in our case, learning.
In this paper, we introduce a so-called DyVT tool (Dynamic social network Visualization Tool) to support spatiotemporal knowledge visualization and discovery in dynamic social networks. The dynamic aspects of social networks refer to contextualized information such as spatial, temporal as well as users’ personalized information. We also define an XML-based target language incorporating emerging formats like DyNetML, KML, and GraphML. It also provides means to express, store and interchange the dynamic aspects of complex dynamic social network data. Based on this language, users can animate and personalize spatiotemporal knowledge extracted from social network data like email threads or blogs. In addition, a Java based graphical user interface is also available to enable nonexperienced users to customize knowledge visualization easily. A mashup with Google maps for spatiotemporal visualization is provided. With this tool spatiotemporal knowledge on an IBM DB2 Mailing list database containing 69 mailing lists and 56389 mails altogether is well explored.
Information and communication technology (ICT) skills/ competence frameworks are an important prerequisite for ICT competence development and related quality assurance for recognition and transferability of qualifications. In this paper it is argued the importance of interoperability through common or explicit semantics of ICT competence profiles. This requires modelling on basis of shared frameworks. Thus, existing frameworks have been analysed. The paper presents underlying structure and data models of some prominent systems that have achieved status of proprietary standards. Moreover, a conceptual model is derived on basis of a comprehensive analysis of respective meta data of skill/ competence grids. Presented work provides the theoretical foundation for further development of information systems for the management of knowledge, skills, competences and qualification. Applications are skill/competence catalogues and repositories, as well as web services for data exchange between human resource information systems.
The paper outlines how attention metadata enables a tight integration between organisational knowledge stores and human resource management on the one hand and learning management system in corporate contexts on the other hand. The approach relies on an extension of AttentionXML, a metadata standard to capture the attention a user spends on digital content. We suggest relying on attention metadata to unobtrusively capture the attention of employees at the workplace (e.g. through workflow systems, knowledge management systems, human resource management systems, etc.) and use the captured information to enable a targeted steering of learning process of the employee in companies in accordance with company’s and employee’s aims and goals.
We explored how the intended purpose of a knowledge model can influence the modelling process and in particular, how it impacts on the choice points of the underlying modelling methodology. We introduce a classification of knowledge models according to their intended scope, expressiveness and degree of acceptance. As a result, we aim to define critical success factors of methodologies for ontologies that are built by domain experts and that can be used as a basis for knowledge enabled (software) systems
Two fields of research are heavily interested in developing visualizations for helping users coping with complex tasks and ill-structured subject matter resources: knowledge visualization and information visualization. The goal of knowledge visualization is to assist students in learning and problem solving by providing tools for fostering externalized cognition. The goal of information visualization is to provide knowledge-based access to information resources and help users in making sense of the resources they are looking for in information retrieval. The contribution draws attention to digital concept maps as cognitive tools which may provide a basis for the development of synergistic approaches that may help visualizing, accessing, and managing both knowledge and information and foster resourcebased learning.
In Higher Education and Research Organisations (HEROs), one of the most important activities in the R&D process is the effective management of knowledge transference. A correct analysis and diagnosis of that process through knowledge management methodology is essential for the correct orientation of organisation strategy. The aim of this paper is to describe the analysis carried out in order to diagnose the research & development & transference (R&D&T) activities at a public university in Spain. The diagnosis analyses the key phases in the knowledge transference process, because these different stages define important implications for the monitoring of the intellectual capital and the organisation’s performance. Also with in the diagnostic analysis preformed here an methodological innovation is introduced related with the cause and effect relations of the knowledge collaboration and a process witch deals mainly with intangibles
Within a business environment, where the fast and reliable access to knowledge is a key success factor, an efficient handling of the organizational knowledge is crucial. Therefore the need for methods and techniques, which allow to structure and maintain complex knowledge bases according to the requirements emerging from the daily work have a high priority. This article provides a business process oriented approach to structure organizational knowledge and information bases. The approach was developed within applied research in the industrial, service and administrative sector. Following this approach, three different types of knowledge structures and their visualization have been developed by the Fraunhofer IPK and are currently applied and tested in organizations. Beside the approach itself, these three types of knowledge structure and the cases of application shall be introduced here.