Evaluation of a semantic similarity measure for natural language spatial relations

Abstract

Consistent and flawless communication between humans and machines is the precondition for a computer to process instructions correctly, While machines use well-defined languages and formal rules to process information, humans prefer natural language expressions with vague semantics. Similarity comparisons are central to the human way of thinking: we use similarity for reasoning on new information or new situations by comparing them to knowledge gained from similar experiences in the past. It is necessary to overcome the differences in representing and processing information to avoid communication errors and computation failures. We introduce an approach to formalize the semantics of natural language spatial relations and specify it in a computational model which allows for similarity comparisons. This paper describes an experiment that investigates human similarity perception between spatial relations and compares it to the similarity determined by the our semantic similarity measure. © Springer-Verlag Berlin Heidelberg 2007.