. This paper suggests that a distinction between knowledge acquisition methods should be made. On the one hand there are methods which aim to help the expert and knowledge engineer analyse what knowledge is involved in solving a particular type of problem and how this problem solving is carried

Traditional knowledge acquisition tools rely either on conversational textual interfaces or on graphical editing of rule and description structures. Both these methods divorce the user from the actual concrete problem-solving situations where the knowledge is created and applied. We show how a

In this paper we analyse how noise can affect Knowledge Acquisition from a Machine Learning perspective. We present some methods to detect and treat noise that goes beyond modulating numerical coefficients and show that noise cannot be viewed as a single entity. There are several different types

Situated cognition is not a mere philosophical concern: it has pragmatic implications for current practice in knowledge acquisition. Tools must move from being design-focused to being maintenance-focused. Reuse-based approaches (e.g. using problem solving methods) will fail unless the reused

This paper addresses the knowledge acquisition bottleneck. It first takes an information-theoretic view of knowledge acquisition as having a basis in randomization theory and subsequently gives practical examples. The examples are taken from the field of expert compiler technology. Such technology

In this paper we will present a design model (in the sense of KADS) for the domain of technical diagnosis. Based on this we will describe the fully implemented expert system shell MOLTKE 3.0, which integrates common knowledge acquisition methods with techniques developed in the fields of Model

, and analyze in information systems, because of its fluency with context shifts, and its underspecification [3]. Thinking about knowledge acquisition methods—from geographic information retrieval from big data to dedicated user generated content—they seem to involve several disciplines such as computational

Although vast amounts of textual data are freely available, many NLP algorithms exploit only a minute percentage of it. In this paper, we study the challenges of working at the terascale. We present an algorithm, designed for the terascale, for mining is-a relations that achieves similar performance to a state-of-the-art linguistically-rich method. We focus on the accuracy of these two systems as a function of processing time and corpus size. 1

This paper argues that automated knowledge acquisition for diagnosis has had limited success in both failure-driven diagnosis and model-based diagnosis. The Hakeem algorithm for the automated generation of causal models from fault-based knowledge is introduced. An example of fault-based knowledge

Knowledge acquisition is a constructive modeling process, not simply a matter of “expertise transfer. ” Consistent with this perspective, we advocate knowledge acquisition practices and tools that facilitate active collaboration between expert and knowledge engineer and that support knowledge