Partition-Based Consequence Finding

There is a growing interest in building large knowledge bases. Dealing with a huge amount of knowledge, two problems can be encountered in real domains. The first case is that knowledge is originally centralized so that one can access the whole knowledge but the size of the knowledge base is too huge to be handled. The second case is that knowledge is distributed in several sources so that it is hard or impossible to immediately access the whole or part of knowledge. We focus here on the case in which a single reasoner might not be able to cope with the entire database, and tries to partitioned the data to improve its scalability, which is likely to happen if the knowledge is partitioned into overlapping but cohesive components. We thus consider distributed reasoning with such structures, each partition collaborating with the other to produce a coherent output. We thus propose a generalization of partition-based theorem proving to partition-based consequence finding (sharing a specification of ``interesting'' consequences), with a sequential and a parallel version. As termination cannot always be ensured in first order, we also investigate bounded searches. Finally we provide an experimental analysis comparing our two variants with the centralized case using some automated process to decompose the theory, and show that for most problems, partitioning the data can indeed increase the efficiency, though proper choice of the decomposition (and especially of the starting point of the algorithm) can be difficult.