Towards Knowledge Discovery in the Semantic Web

In the past, data mining and machine learning research has developed various techniques to learn on data and to extract patterns from data to support decision makers in various tasks, such as customer profiling, targeted marketing, store layout, and fraud detection (Tan et al., 2005, p.1). In addition, the World Wide Web increasingly offers distributed information that can be useful for strategic, tactical or operational decisions, including news, events, financial information, information about competitors as well as information about the social networks of customers and employees etc. The Web thus has the potential for a high impact on competitive actions and competitive dynamics of enterprises that should utilize this information. However, the growing amount of these distributed information resources leads to a dilemma:”... the more distributed and independently managed that resources on the Web become, the greater is their potential value, but the harder it is to extract value...” (Singh and Huhns, 2005, p.7). On the one hand the human ability for information processing is limited (Edelmann, 2000, p.168), whilst otherwise the amount of available information of the Web increases exponentially, which leads to increasing information saturation (Krcmar, 2004, p.52). In this context, it becomes more and more important to detect useful patterns in the Web, thus use it as a rich source for data mining (Berendt et al., 2002; Han and Kamber, 2006, p.628) in addition to company internal databases. The extraction of information and interesting patterns out of the Web is a complex task, because the current Web is mainly utilized for human consumption. This means that the available information is represented by mark-up languages such as XHTML1 and its predecessors that describe only a visual presentation. Unfortunately, these languages are not sufficient to let software agents ”under-