Inference fusion: a hybrid approach to taxonomic reasoning.
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HU, B., ARANA, I. and COMPATANGELO, E., 2003. Inference fusion: a hybrid approach to taxonomic reasoning. In: I. RUSSELL and S. HALLER, eds. Proceedings of the 16th International FLAIRS conference. 12-14 May 2003. St. Augustine, Florida: AAAI. pp. 103-107.
We present a hybrid way to extend taxonomic reasoning using inference fusion, i.e. the dynamic combination of inferences from distributed heterogeneous reasoners. Our approach integrates results from a DL-based taxonomic reasoner with results from a constraint solver. Inference fusion is carried out by (i) parsing heterogeneous input knowledge, producing suitable homogeneous subset of the input knowledge for each specialised reasoner; (ii) processing the homogeneous knowledge, collecting the reasoning 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 to the ontological reasoning and demonstrate our ideas by proposing a hybrid modelling languages, DL(D)=S, and illustrating its use by means of examples.