A new approach for measuring semantic similarity of ontology concepts using dynamic programming

TL;DR

This paper introduces a novel dynamic programming-based method for measuring semantic similarity between ontology concepts, enabling more flexible and accurate similarity assessments within hierarchical structures.

Contribution

It proposes a new semantic similarity measure using dynamic programming and a weight allocation function, improving over existing methods by capturing nuanced differences.

Findings

Demonstrates the effectiveness of the proposed method through experimental comparison.

Shows how the method overcomes limitations of previous similarity measures.

Provides a flexible approach to quantify semantic similarity with adjustable weights.

Abstract

Today, with the emergence of semantic web technologies and increasing of information quantity, searching for information based on the semantic web has become a fertile area of research. For this reason, a large number of studies are performed based on the measure of semantic similarity. Therefore, in this paper, we propose a new method of semantic similarity measuring which uses the dynamic programming to compute the semantic distance between any two concepts defined in the same hierarchy of ontology. Then, we base on this result to compute the semantic similarity. Finally, we present an experimental comparison between our method and other methods of similarity measuring. Where we will show the limits of these methods and how we avoid them with our method. This one bases on a function of weight allocation, which allows finding different rate of semantic similarity between a given concept and two other sibling concepts which is impossible using the other methods.