Knowledge selection with neural networks

Knowledge selection (KS) is a completely new method, which was developed at the department of Knowledge Engineering of the University of Vienna, aiming at selecting relevant knowledge out of a knowledge base for a particular task. This method is important for supporting the efficient (re-)use of knowledge in knowledge management systems. KS is realised by three filters: identification selects knowledge items according to syntactical properties of the query; adaption uses background knowledge for the filtering; and prediction tries to predict future queries for a small number of time steps. In this paper neural network solutions for KS together with an KS-implementation in the area of computer security is presented