The access to information contained in possibly large knowledge bases is a crucial factor in the usability of such a knowledge base. In this paper, we present a method to select information relevant for a query in knowledge bases where the information is represented in a rule-based way. An approach based on artificial neural networks is used to pre-select the set of relevant rules, thus facilitating the task of the inference mechanism by restricting the search space to be traversed considerably. In addition to the information contained in the query itself, data derived from the environment in which the query is situated is used to further trim down the search space. Sources for this derivation process are data about the task under investigation as well as the history of user interactions.

We refer to the first way of diminishing the search space via the query as identification; the second one is referred to as adaptation, since the selection process is adapted to the current task. The third one, taking into account the history of interactions between user and knowledge base, is called prediction, aiming at a possible prediction of the next query, or a subset of rules relevant for the next query.

An implementation of the artificial neural networks used for these tasks is based on ICSIM, a connectionist simulator developed at ICS1.