Integrating Different Conceptualizations for Heterogeneous Knowledge

In this paper we argue that the model-based development of knowledge-based systems built to work in partially uncertain domains benefits from the fusion of different conceptualizations for certain and uncertain parts of the required knowledge. We present conceptualizations that have proven to be useful, namely the KADS model of expertise and a causal model of uncertainty that reflects well known approaches to uncertain reasoning like Bayesian belief nets. We propose an extension of existing specification languages that aims at an integration of these conceptualizations in a common knowledge model. We present parts of the analysis and specification of a rock classification problem as an example demonstrating the demand for the combination of different conceptualizations

A Flexible Framework for Uncertain Expertise

In this paper we argue that the development of knowledge-based systems built to work in partially uncertain domains benefit from the use of different conceptualisations for certain and uncertain parts of the knowledge. We present conceptualisations that have proven to be useful, namely the KADS model of expertise and a causal model of uncertainty that reflects well known approaches to uncertain reasoning like Bayesian belief nets. After a brief introduction to these conceptualisations we propose a translation approach that aims at an integration of these conceptualisations in a common knowledge model that can be used in a knowledge engineering process

Bridging Gaps in Models of Expertise

This paper presents an approach to the explicit integration of uncertain reasoning mechanisms into conventional KADS-based models of expertise. We assume that uncertainty is present in the model of expertise in the sense that terminological knowledge is available but some assertions cannot be determined due to uncertainty. These gaps can be bridged in a three-step inference scheme using a general model of uncertainty in which knowledge is represented as sets of hypotheses

AkuVis: Interactive visualization of acoustic data

Fehr R, Börner K, Wachsmuth I. AkuVis: Interactive visualization of acoustic data. In: Haasis H-D, Ranze CK, eds. Umweltinformatik '98: Vernetzte Strukturen in Informatik, Umwelt und Wirtschaft. Vol 2. Marburg: Metropolis-Verlag; 1998: 722-728.AkuVis (Interactive Visualization of Acoustic Data) is a joint project involving the Artificial Intelligence as well as the Visualization Laboratory at the University of Bielefeld, government researchers from the Institute of Public Health NRW and the Local Environmental Agency Bielefeld as well as the German TÜV. The project seeks to create a highly interactive virtual environment of modelled acoustic data in order to sensitize and improve human decision-making in real world tasks. In particular, it attempts to enhance the integrated understanding of noise data as a basis for governmental decisions about noise protection regulations for new streets, industrial areas etc