Facilitating DL-based hybrid reasoning with inference fusion.
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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 (DL(D)=S) and a reasoning system (Concor) which uses knowledge bases written in DL(D)=S and supports hybrid reasoning. We illustrate our ideas with an example.