Inside the LOOM description classifier

Abstract

This paper presents a symbol level account of some of the representation and reasoning structures within the LOOM knowledge representation system. Reasoning in LOOM centers around a classifier whose primary function is to construct a taxonomy of all descriptions that have been entered into the system. The LOOM classifier is unique in that it constructs a separate taxonomy for each of seven kinds of non-composite descriptions, and uses a marker passing algorithm to replace the quadratic time subsumption test found in most classifiers with a linear time test. We briefly illustrate how the selection of data structures within LOOM impacts the completeness of the classification algorithm, and we describe the LOOM option that allows concepts to be reasoned with in either a forward-chaining or a backward-chaining mode.