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Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching

arXiv 2207.11324 ↗First posted 22 Jul 2022cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 22 Jul 2022.

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References · 14

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  12. 2016Lifted Rule Injection for Relation Embeddingscited by 22
2 more
  1. 2019Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spacescited by 14
  2. 2019Wasserstein distances for evaluating cross-lingual embeddingscited by 1

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