An Elephant in the Dark: Creating Semantic Representations of Perceived Data with Conceptual Spaces

Abstract

This paper discusses the task of creating semantic representations to describe numerical observations using conceptual spaces. The theory of conceptual spaces is considered as a semantic representation to conceptualise the perceived numerical information and to infer linguistic descriptions. We propose a data-driven approach to construct conceptual spaces from numerical data automatically. First, the elements of a conceptual space are derived based on a set of numerical observations in order to semantically represent the concepts of a given data set. This data-driven conceptual space is then employed for the task of semantic inference, in order to linguistically describe unknown perceived observations.