Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching

TL;DR

This paper investigates the use of Wasserstein distance on concept embeddings to improve ontology matching by capturing semantic similarities beyond shallow string comparisons, showing competitive results in benchmark tests.

Contribution

It introduces a novel approach applying Wasserstein distance to embedded ontology elements, enhancing semantic similarity measurement for ontology matching.

Findings

Wasserstein distance effectively captures semantic similarity in ontology elements.

The approach achieves competitive results on OAEI and MSE benchmarks.

Embedding-based distance metrics outperform traditional string-based methods.

Abstract

Measuring the distance between ontological elements is fundamental for ontology matching. String-based distance metrics are notorious for shallow syntactic matching. In this exploratory study, we investigate Wasserstein distance targeting continuous space that can incorporate various types of information. We use a pre-trained word embeddings system to embed ontology element labels. We examine the effectiveness of Wasserstein distance for measuring similarity between ontologies, and discovering and refining matchings between individual elements. Our experiments with the OAEI conference track and MSE benchmarks achieved competitive results compared to the leading systems.