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Journal on Data Semantics
Erschienen in: Journal on Data Semantics 3-4/2021

16.10.2021 | Original Article

Measuring Clusters of Labels in an Embedding Space to Refine Relations in Ontology Alignment

verfasst von: Molka Tounsi Dhouib, Catherine Faron, Andrea G. B. Tettamanzi

Erschienen in: Journal on Data Semantics | Ausgabe 3-4/2021

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Abstract

Ontology alignment plays a key role in the management of heterogeneous data sources and metadata. In this context, various ontology alignment techniques have been proposed to discover correspondences between the entities of different ontologies. This paper proposes a new ontology alignment approach based on a set of rules exploiting the embedding space and measuring clusters of labels to discover the relationship between entities. We tested our system on the OAEI conference complex alignment benchmark track and then applied it to aligning ontologies in a real-world case study. The experimental results show that the combination of word embedding and a measure of dispersion of the clusters of labels, which we call the radius measure, makes it possible to determine, with good accuracy, not only equivalence relations, but also hierarchical relations between entities.
Vorheriger Artikel Defining and Detecting Complex Changes on RDF(S) Knowledge Bases
Nächster Artikel Correction to: AI-Empowered Process Mining for Complex Application Scenarios: Survey and Discussion

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Metadaten
Titel
Measuring Clusters of Labels in an Embedding Space to Refine Relations in Ontology Alignment
verfasst von
Molka Tounsi Dhouib
Catherine Faron
Andrea G. B. Tettamanzi
Publikationsdatum
16.10.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal on Data Semantics / Ausgabe 3-4/2021
Print ISSN: 1861-2032
Elektronische ISSN: 1861-2040
DOI
https://doi.org/10.1007/s13740-021-00137-8

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