Inference fusion: a hybrid approach to taxonomic reasoning.

Abstract

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.

Citation

HU, B., COMPATANGELO, E. and ARANA, I. 2003. Inference fusion: a hybrid approach to taxonomic reasoning. In Russell, I. and Haller, S. (eds.) Proceedings of the 16th International conference of the Florida Artificial Intelligence Research Society (FLAIRS 2003), 12-14 May 2003, St. Augustine, USA. Palo Alto: AAAI Press [online], pages 103-107. Available from: https://aaai.org/Papers/FLAIRS/2003/Flairs03-021.pdf

• Conference Name: 16th International conference of the Florida Artificial Intelligence Research Society (FLAIRS 2003)
• Conference Location: St. Augustine, USA
• Start Date: May 12, 2003
• End Date: May 14, 2003
• Acceptance Date: May 12, 2003
• Online Publication Date: Dec 31, 2003
• Publication Date: Dec 31, 2003
• Deposit Date: Feb 24, 2009
• Publicly Available Date: Feb 24, 2009
• Publisher: Association for the Advancement of Artificial Intelligence
• Pages: 103-107
• ISBN: 9781577351771
• Keywords: Taxonomic reasoning; Inference fusion; Ontology reasoning; Description logics
• Public URL: http://hdl.handle.net/10059/308
• Publisher URL: https://aaai.org/Papers/FLAIRS/2003/Flairs03-021.pdf