Facilitating DL-based hybrid reasoning with inference fusion

We present an extension to DL-based taxonomic reasoning by means of the proposed inference fusion, i.e. the dynamic combination of inferences from distributed heterogeneous reasoners. Our approach integrates results from a DL-based system with results from a constraint solver under the direction of a global reasoning coordinator. Inference fusion is performed by (i) processing heterogeneous input knowledge, producing suitable homogeneous input knowledge for each specialised reasoner; (ii) activating each reasoner when necessary, collecting its results and passing them to the other reasoner if appropriate; (iii) combining the results of the two reasoners. We discuss the benefits of our approach and demonstrate our ideas by proposing a language (DE(D)IS) and a reasoning system (CONCOR) which uses knowledge bases written in VL(D)/S and supports hybrid reasoning. We illustrate our ideas with an example.