Inference fusion: a hybrid approach to taxonomic reasoning
Hu, Bo and Compatangelo, Ernesto and Arana, Ines (2003) Inference fusion: a hybrid approach to taxonomic reasoning. In Proc. of the 16th International Florida Artificial Intelligence Research Symposium Conference (FLAIRS'03), pages 103-107, AAAI Press, 2003.
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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 ontological reasoning and demonstrate our ideas by proposing reasoners. We discuss the benefits of our a hybrid modelling languages, DL(D)=S, and illustrating its use by means of examples.
|Subjects:||AKT Challenges > Knowledge reuse|
AKT Challenges > Knowledge modelling
|Deposited By:||Ajit, Suraj|
|Deposited On:||24 February 2004|
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