Valentin Zacharias

KAON - Towards a Large Scale Semantic Web

The Semantic Web will bring structure to the content of Web pages, being an extension of the current Web, in which information is given a well-defined meaning. Especially within e-commerce applications, Semantic Web technologies in the form of ontologies and metadata are becoming increasingly prevalent and important. This paper introduce KAON - the Karlsruhe Ontology and Semantic WebTool Suite. KAON is developed jointly within several EU-funded projects and specifically designed to provide the ontology and metadata infrastructure needed for building, using and accessing semantics-driven applications on the Web and on your desktop.

Clustering Ontology-Based Metadata in the Semantic Web

The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computersr and people to work in cooperation. Recently, different applications based on this vision have been designed, e.g. in the fields of knowledge management, community web portals, e-learning, multimedia retrieval, etc. It is obvious that the complex metadata descriptions generated on the basis of pre-defined ontologies serve as perfect input data for machine learning techniques. In this paper we propose an approach for clustering ontology-based metadata. Main contributions of this paper are the definition of a set of similarity measures for comparing ontology-based metadata and an application study using these measures within a hierarchical clustering algorithm.

Applying quantified self approaches to support reflective learning

This paper presents a framework for technical support of reflective learning, derived from a unification of reflective learning theory with a conceptual framework of Quantified Self tools -- tools for collecting personally relevant information for gaining self-knowledge. Reflective learning means returning to and evaluating past experiences in order to promote continuous learning and improve future experiences. Whilst the reflective learning theories do not sufficiently consider technical support, Quantified Self (QS) approaches are rather experimental and the many emergent tools are disconnected from the goals and benefits of their use. This paper brings these two strands into one unified framework that shows how QS approaches can support reflective learning processes on the one hand and how reflective learning can inform the design of new QS tools for informal learning purposes on the other hand.

Development and Verification of Rule Based Systems -- A Survey of Developers

While there is great interest in rule based systems and their development, there is little data about the tools and methods used and the issues facing the development of these systems. To address this deficiency, this paper presents the results from a survey of developers of rule based systems. The results from the survey give an overview of the methods and tools used for development and the major issues hindering the development of rule based systems. Recommendations for possible future research directions are presented. The results point to verification and validation, debugging and overall tool support as the main issues negatively affecting the development of rule based systems. Further a lack of methodologies that appropriately support developers of these systems was found.

Live interest meter: learning from quantified feedback in mass lectures

There is currently little or no support for speakers to learn by reflection when addressing a big audience, like mass lectures, virtual courses or conferences. Reliable feedback from the audience could improve personal skills and work performance. To address this shortcoming we have developed the Live Interest Meter App (LIM App) that supports the gathering, aggregation and visualization of feedback. This application allows audience members to easily provide and quantify their feedback through a simple meter. We conducted several experimental tests to investigate the acceptance and perceived usefulness of the LIM App and a user study in an academic setting to inform its further development. The results of the study illustriate the potential of the LIM App to be used in such scenarios. Main findings show the need for motivating students to use the application, the readiness of presenters to learn retrospectively, and distraction as the main concern of end users.

Using machine learning to support continuous ontology development

This paper presents novel algorithms to support the continuous development of ontologies; i.e. the development of ontologies during their use in social semantic bookmarking, semantic wiki or other social semantic applications. Our goal is to assist users in placing a newly added concept in a concept hierarchy. The proposed algorithm is evaluated using a data set from Wikipedia and provides good quality recommendation. These results point to novel possibilities to apply machine learning technologies to support social semantic applications.

Semantic Technologies and Cloud Computing

Cloud computing has become a generic umbrella term for the flexible delivery of IT resources—such as storage, computing power, software development platforms, and applications—as services over the Internet. The foremost innovation is that the IT infrastructure no longer lies with the user, breaking up the previously monolithic ownership and administrative control of its assets. The combination of cloud computing and semantic technologies holds great potential. In this chapter, we analyze three ways in which cloud computing and semantic technologies can be combined: (1) building on cloud computing technologies, e.g. from the area of distributed computing, to realize better semantic applications and enable semantic technologies to scale to ever larger data sets, (2) delivering semantic technologies as services in the cloud, and (3) using semantic technologies to improve cloud computing, in particular to further improve automatic data-center management. For each of these dimensions we identify challenges and opportunities, provide a survey, and present a research roadmap.

Tackling the Debugging Challenge of Rule Based Systems

Rule based systems are often presented as a simpler and more natural way to build computer systems - compared to both imperative programming and other logic formalisms. However, with respect to finding and preventing faults this promise of simplicity remains elusive.

The Agile Development of Rule Bases

Recently, with the large-scale practical use of business rule systems and the interest of the Semantic Web community in rule languages, there is an increasing need for methods and tools supporting the development of rule-based systems. Existing methodologies fail to address the challenges posed by modern development processes in these areas, namely, the increasing number of end-user programmers and the increasing interest in iterative methods. To address these challenges, we propose and discuss the adoption of agile methods for the development of rule-based systems. The main contribution of this paper is three development principles for and changes to the Extreme Programming development process to make it suitable for the development of rule-based systems.

Using the Ontology Maturing Process Model for Searching, Managing and Retrieving Resources with Semantic Technologies

Semantic technologies are very helpful in improving existing systems for searching, managing and retrieving of resources, e.g. image search, bookmarking or expert finder systems. They enhance these systems through background knowledge stored in ontologies. However, in most cases, resources in these systems change very fast. In consequence, they require a dynamic and agile change of underlying ontologies. Also, the formality of these ontologies must fit the users needs and capabilities and must be appropriate and usable. Therefore, a continuous, collaborative and work or task integrated development of these ontologies is required. In this paper, we present how these requirements occur in real world applications and how they are solved and implemented using our Ontology Maturing Process Model.