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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching
22 Jul 2022
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.
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Details
- Title
- Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching
- Creators
- Yuan AnAlex KalinowskiJane Greenberg
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Other Identifier
- 991020532100804721