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.
This is the latest version of this eprint.
This document is not available online.
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 |
|---|---|
| ID Code: | 286 |
| Deposited By: | Ajit, Suraj |
| Deposited On: | 24 February 2004 |
Available Versions of this Item
- Inference fusion: a hybrid approach to taxonomic reasoning (deposited 24 February 2004) [Currently Displayed]
Contact the site administrator at: hg@ecs.soton.ac.uk