Two Approaches to Merging Knowledge Bases

We present a framework for expressing different merging operators for belief sets. This framework is a generalisation of our earlier work concerning consistency-based belief revision and contraction. Two distinct merging operators are identified: in the first approach, belief sources are consistently combined so that the result of merging knowledge bases K1, . . . , Kn is a maximal consistent (if possible) set of formulas comprising the joint knowledge of the knowledge bases. This approach then accords to one’s intuitions as to what a “merge” operator should do. The second approach is more akin to a generalised belief revision operator: Knowledge bases K1, . . . , Kn are “projected” onto another (in the simplest case the trivially true knowledge base). In both cases, we consider the incorporation of entailment-based and consistency-based integrity constraints. Properties of these operators are investigated, primarily by comparing their properties with postulates that have b...

• Type: Conference
• Year: 2004
• Where: JELIA
• Authors: James P. Delgrande, Torsten Schaub
• Keywords: Artificial Intelligence, Belief Revision, JELIA 2004, Knowledge Base, Merging Operators