In this position paper we present a vision of how the stories that people tell in Internet weblogs can be used directly for automated commonsense reasoning, specifically to support the core envisionment functions of event prediction, explanation, and imagination.
The stories told among members of an organization are an effective instrument for knowledge socialization, the sharing of experiences through social mechanisms. However, the utility of stories for organizational learning is limited due to the difficulties in acquiring stories that are relevant to the practices of an organization, identifying the learning goals that these stories serve, and delivering these stories to the right people and the right time in a manner that best facilitates learning. In this paper we outline a vision for story-based organizational learning in the future, and describe three areas where intelligent technologies can be applied to automate story management practices in support of organizational learning. First, we describe automated story capture technologies that identify narratives of people’s experiences within the context of a larger discourse. Second, we describe automated retrieval technologies that identify stories that are relevant to specific educational needs. Third, we describe how stories can be transformed into effective story-based learning environments with minimal development costs.
An analysis of the development of the concept of knowledge is provided involving the consequences of the scientific and the management revolution. The situation of conceiving knowledge in the industrial society is outlined. An analysis of important factorst leading to he present sitation is provided focusing on the role of the IT-revolution, esp. Artificial intelligence and knowledge based systems and organisational learning as a key factor of value creation. Finally the present situation is outlined ending with a conception of what knowledge management is.
This article outlines new technologies in the areas of automated expertise finding, expert network discover, virtual place-based collaboration, and automated question answering. We illustrate each of these areas with implemented and in some cases empirically evaluated systems. Collectively, these illustrate new methods for automatic discovery of knowledge, experts, and communities in an effective and efficient manner.