Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
This provides a more robust evaluation framework for ontology matching in bioinformatics, though it is incremental as it builds on existing OAEI efforts.
The paper addresses limitations in evaluating ontology matching systems, particularly for machine learning approaches, by introducing five new biomedical tasks with curated reference mappings for equivalence and subsumption matching, and reports evaluation results for various systems as part of a publicly available benchmark.
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 BioML track at OAEI 2022.