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New Generation Computing

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06-01-2023

A Graph-Based Ontology Matching Framework

Authors: Fatmana Şentürk, Vecdi Aytac

Published in: New Generation Computing

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Abstract

Ontologies are domain-specific metadata that describe relationships between a specific field’s properties, sample data of this field, and properties developed for many different purposes. Also, ontologies can be defined by other names within the same domain. Ontology matching algorithms eliminate definition differences and find similarities between existing ontologies. Ontology matching algorithms are used especially for information management, data integration, information extraction, etc. In this study, a graph-based framework is proposed to match large ontologies. This framework is aimed to divide the large ontologies into small pieces and then matches them using sub-graph mining algorithms. Karger algorithm and CP (clique percolation and nearest neighbor) algorithm are used to divide large ontologies. Both algorithms were applied to ontologies for the first time. In the next step, these obtained sub-parts are matched by using sub-graph mining algorithms. GraMi and gSpan algorithms were selected and were used for the first time in the field of ontology matching. We validated our framework using Anatomy and Conference data sets. Also, the proposed framework is compared with widely used in the literature AML and Falcon-AO matching algorithms. According to obtained the results, it is seen that GraMi is better than matching algorithms.

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Footnotes
1
Ontology Alignment Evaluation Initiative(OAEI): https://​oaei.​ontologymatching​.​org/​ [Accessed August, 2022].
 
2
Ontology Matching: http://​www.​ontologymatching​.​org/​ [Accessed August, 2022].
 
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Metadata
Title
A Graph-Based Ontology Matching Framework
Authors
Fatmana Şentürk
Vecdi Aytac
Publication date
06-01-2023
Publisher
Ohmsha
Published in
New Generation Computing
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-022-00200-3

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