A Method for Measuring Semantic Similarity of Concepts in the Same Ontology

Abstract

The present methods for measuring concepts semantic similarity only focus on certain influencing factors, have poor convergence performances and canpsilat calculate accurately. This paper compares three kinds of ontology-based semantic similarity calculation models. On this basis, an improved algorithm that inherits the distance-based calculation model is proposed. In this approach, node depth, local density and node attributes are newly quantified and the granularity degree of clusters is firstly combined with other five factors: local density, node depth, link type, link strength, node attribute. The experimental results show that this method provides an effective quantification for the semantic relationships, and can calculate semantic similarity more precisely.