A Theory of Modeling Semantic Uncertainty in Label Representation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9978)

Abstract

A new theory of modeling the uncertainty associated with vague concepts is introduced. We consider the problem of quantifying an agents uncertainty concerning which labels are appropriate to describe a given observation. This can be regarded as a simplified model of natural language communication. Semantic meaning conveyed by high-level knowledge representation is often inherently uncertain. Such uncertainty is referred to semantic uncertainty and dominated by fuzzy modeling. In this framework, from an epistemic point of view, labels are precise and uncertainty comes from the undecidable boundary between labels in agents conceptual space. In this framework the boundary is regarded as a random variable and it can be modeled by a probability distribution. We also propose a functional calculus to measure how appropriate of using a certain label to describe an observation. In this way, a vague concept can be represented by a distribution on the labels. The new theory is verified by applying it to the vague category game.

Keywords

Label differentiation Boundary distribution Linguistic label Label image Category game 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Intelligent Computing and Machine Learning Lab, School of ASEEBeihang UniversityBeijingChina
  2. 2.School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
  3. 3.École Centrale de PékinBeihang UniversityBeijingChina

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