Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between...

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Bibliographic Details
Published in:Information (Basel) 2021-11, Vol.12 (11), p.487
Main Authors: Al-Yadumi, Sohaib, Goh, Wei-Wei, Tan, Ee-Xion, Jhanjhi, Noor Zaman, Boursier, Patrice
Format: Article
Language:English
Subjects:
aggregation strategy
Algorithms
background knowledge
biomedical ontologies
Correspondence
Data integration
Heterogeneity
indirect matching
Interoperability
mapping composition
Matching
Ontology
ontology alignment
Semantic web
Semantics
Software
Tracks (paths)
Citations: Items that this one cites
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Summary:Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.
ISSN:2078-2489
2078-2489
DOI:10.3390/info12110487