Paper Info
Title
Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
Abstract
Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new Bio-ML track at OAEI 2022. Resource type: Ontology Matching Dataset License: CC BY 4.0 International DOI: https://doi.org/10.5281/zenodo.6510086 Documentation: https://krr-oxford.github.io/DeepOnto/#/om_resources OAEI track: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/
Year
DOI
Venue
2022
10.1007/978-3-031-19433-7_33
The Semantic Web – ISWC 2022
Keywords
DocType
ISSN
Ontology Alignment, Equivalence matching, Subsumption matching, Evaluation resource, Biomedical ontology, OAEI
Conference
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Yuan He100.34
J Chen213930.64
Hang Dong300.34
Ernesto Jiménez-Ruiz4112084.14
Ali Hadian500.34
Ian Horrocks6117311086.65