Level 2/3 fusion in conceptual spaces

Abstract

This paper presents a novel approach to data fusion knowledge representation using conceptual spaces. Conceptual spaces represent knowledge geometrically in multiple domains, each domain consisting of multiple dimensions with an associated distance metric and corresponding similarity measure. Complex concepts such as those required for level 2/3 fusion are described by multiple property regions within these domains, along with the property correlations and salience weights. These concepts are mapped into points in the unit hypercube that capture all of their essential elements. Observations are also mapped into points in the same unit hypercube. The relative similarity of observations to concepts can then be calculated using the fuzzy subsethood measure