Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching
This addresses ontology matching for semantic web applications, but is incremental as it applies an existing distance metric to a known bottleneck.
The study tackled the problem of measuring distance between ontological elements for ontology matching by investigating Wasserstein distance in continuous space using pre-trained word embeddings, achieving competitive results on OAEI conference track and MSE benchmarks.
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.