Declarative Rules for Annotated Expert Knowledge in Change Management

In this paper, we use declarative and domain–specific languages for representing expert knowledge in the field of change management in organisational psychology. Expert rules obtained in practical case studies are represented as declarative rules in a deductive database. The expert rules are annotated by information describing their provenance and confidence. Additional provenance information for the whole – or parts of the – rule base can be given by ontologies. Deductive databases allow for declaratively defining the semantics of the expert knowledge with rules; the evaluation of the rules can be optimised and the inference mechanisms could be changed, since they are specified in an abstract way. As the logical syntax of rules had been a problem in previous applications of deductive databases, we use specially designed domain– specific languages to make the rule syntax easier for non–programmers. The semantics of the whole knowledge base is declarative. The rules are written declaratively in an extension Datalog∗ of the well–known deductive database language Datalog on the data level, and additional Datalog∗ rules can configure the processing of the annotated rules and the ontologies. 1998 ACM Subject Classification H.2 Database Management, I.2.1 Applications and Expert Systems, I.2.4 Knowledge Representation Formalisms and Methods