A method for temporal knowledge conversion

Abstract

In this paper we present a new method for temporal knowledge conversion, called TCon. The main aim of our approach is to perform a transition, i.e. conversion, of temporal complex patterns in multivariate time series to a linguistic, for human beings understandable description of the patterns. The main idea for the detection of those complex patterns lies in breaking down a highly structured and complex problem into several subtasks. Therefore, several abstraction levels have been introduced where at each level temporal complex patterns are detected successively using exploratory methods, namely unsupervised neural networks together with special visualization techniques. At each level, temporal grammatical rules are extracted. The method TCon was applied to a problem from medicine, sleep apnea. It is a hard problem since quite different patterns may occur, even for the same patient, as well as the duration of each pattern may differ strongly. Altogether, all patterns have been detected and a meaningful description of the patterns was generated. Even some kind of “new” knowledge was found.