While successful knowledge management depends on numerous organizational and psychological aspects, the effective documentation, dissemination and utilization of knowledge recommends the introduction of computerized systems to manage knowledge. The design of such a system requires a notion of knowledge that allows to distinguish it from information as it is handled by traditional information systems. In this paper, a pragmatic notion of knowledge is suggested. On the one hand, it is inspired by some characteristics of knowledge stressed in philosophy. On the other hand, it reflects ideas about knowledge as a corporate asset and as subject of organizational learning. Against this background, a number of requirements which should be fulfilled by a system that manages knowledge are developed. They result in suggestions for the content as well as

While successful knowledge management depends on numerous organisational and psychological aspects, the effective documentation, dissemination and utilisation of knowledge recommends the introduction of computerized systems to manage knowledge. The design of such a system requires a notion of knowledge that allows to distinguish it from information as it is handled by traditional information systems. In this paper, a pragmatic notion of knowledge is suggested. On the one hand, it is inspired by some characteristics of knowledge stressed in philosophy. On the other hand, it reflects ideas about knowledge as a corporate asset and as subject of organisational learning. Against this background, a number of requirements which should be fulfilled by a system that manages knowledge are developed. They result in suggestions for the content as well as for the architecture of a Knowledge Management System (KMS). Different from organisational memory systems, the proposed KMS features a high level...

The problems of information overload from the growth of scholarly literature, and the need to use information technology to manage them, were identified by major writers and scientists over fifty years ago. Yet the main form of scholarly communication, the journal, is still circulated in paper form as it has been for over three hundred years. The economic arguments for using computer and communication technology to overcome these problems through a new form of scientific communication, the electronic or digital journal, were vigorously presented in the 1970s. Experimental trials of digital journals with the technologies of the 1970s and 1980s have not been successful. In the 1990s, the continuing value of current journal systems is again being questioned in terms of soaring library costs, the burden of the current refereeing system and the diminishing returns of journal publication brought about by information overload. This paper presents a fundamental examination of the prerequisites...

In this position paper I consider four topics on which machine learning and philosophy of science can illuminate each other: scienti#c method, simplicity, theoretical terms and scienti#c realism. I conclude by stressing the di#erences between the two disciplines.

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Scientific discourse leaves implicit a vast amount of knowledge, assumes that this background knowledge is taken into account—even taken for granted—and treated as undisputed. In particular, the terminology in the empirical sciences is treated as antecedently understood. The background knowledge surrounding a theory is usually assumed to be true or approximately true. This is in sharp contrast with logic, which explicitly ignores underlying presuppositions and assumes uninterpreted languages. We discuss the problems that background knowledge may cause for the formalization of scientific theories. In particular, we will show how some of these problems can be addressed in the context of the computational representation of scientific theories.