A Semantic Similarity Measure for Expressive Description Logics

TL;DR

This paper introduces a semantic similarity measure for expressive description logics that calculates similarity between concepts and individuals, facilitating clustering in semantic web applications.

Contribution

It presents a totally semantic, numeric-based similarity measure applicable to expressive description logics, enhancing clustering and reasoning tasks.

Findings

Effective for clustering in semantic web domain

Calculates similarity between concepts and individuals

Applicable to symbolic descriptions

Abstract

A totally semantic measure is presented which is able to calculate a similarity value between concept descriptions and also between concept description and individual or between individuals expressed in an expressive description logic. It is applicable on symbolic descriptions although it uses a numeric approach for the calculus. Considering that Description Logics stand as the theoretic framework for the ontological knowledge representation and reasoning, the proposed measure can be effectively used for agglomerative and divisional clustering task applied to the semantic web domain.