Recognition Algorithms for the Loom Classifier
Abstract
Most of today's terminological representation systems implement hybrid
reasoning architectures wherein a concept classifier is employed to
reason about concept definitions, and a separate recognizer is
invoked to compute instantiation relations between concepts and
instances. Whereas most of the existing recognizer algorithms [are] designed
to maximally exploit the reasoning supplied by the concept classifier,
Loom has experimented with recognition strategies that place less
emphasis on the classifier, and rely more on the abilities of Loom's
backward chaining query facility. This paper presents the results of
experiments that test the performance of the Loom algorithms. These
results suggest that, at least for some applications, the Loom approach
to recognition is likely to outperform the classical approach. They
also indicate that for some applications, much better performance can be
achieved by eliminating the recognizer entirely, in favor of a purely
backward chaining architecture. We conclude that no single recognition
algorithm or strategy is best for all applications, and that an
architecture that offers a choice of inference modes is likely to be
more useful than one that offers only a single style of reasoning.
In Proceedings of the Tenth National Conference on Artificial
Intelligence, (AAAI 92), pp. 774-779, 1992.
The full paper is available in postscript. Get Postscript. (6pp)
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