Performance assessment of ontology matching systems for FAIR data

Background Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this ex...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of biomedical semantics Jg. 13; H. 1; S. 1 - 17
Hauptverfasser: van Damme, Philip, Fernández-Breis, Jesualdo Tomás, Benis, Nirupama, Miñarro-Gimenez, Jose Antonio, de Keizer, Nicolette F., Cornet, Ronald
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 15.07.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Analysis
Bioinformatics
Combinatorial Libraries
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Controlled vocabularies
Cystic fibrosis
Data entry
Data Mining and Knowledge Discovery
FAIR data
Interoperability
Matching
Mathematics
Mathematics and Statistics
Medical research
Medical terminology
Medicine, Experimental
Methods
Ontology
Ontology matching
Performance assessment
Performance evaluation
Rare diseases
Semantic interoperability
Semantics
Web Ontology Language-OWL
Netherlands
Rare diseases
Ontology matching
Semantic interoperability
FAIR data
ISSN:2041-1480, 2041-1480
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Background Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision. Results We included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings’ classes belonged to top-level classes that matched. Conclusions Our findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2041-1480
2041-1480
DOI:10.1186/s13326-022-00273-5