Hybrid Model for Semantic Similarity Measurement

A hybrid model is proposed: a structured knowledge representation combining the relational structure of semantic nets with property-based description of feature- or geometric models that supports to model properties and their scope by taxonomic or non-taxonomic relations between a concept and its properties.

Abstract

Expressive knowledge representations with flexible semantic similarity measures are central for the functioning of semantic information retrieval, information integration, matchmaking etc. Existing knowledge representations provide no or not sufficient support to model the scope of properties. While properties in feature- and geometric models always refer to the whole concept, structured representations such as the alignment model provide a limited support for scope by assigning properties to objects which are part of the whole entity. Network models do not support properties at all. In this paper we propose a hybrid model: a structured knowledge representation combining the relational structure of semantic nets with property-based description of feature- or geometric models. It supports to model properties—features or dimensions—and their scope by taxonomic or non-taxonomic relations between a concept and its properties. The similarity measure computes the similarity in consideration of the scope of each property.