Details

Autor(en) / Beteiligte

• He, Yuan
• Chen, Jiaoyan
• Dong, Hang
• Jiménez-Ruiz, Ernesto
• Hadian, Ali
• Horrocks, Ian
• Sattler, Ulrike
• Presutti, Valentina
• Almeida, João Paulo A.
• Pirrò, Giuseppe
• Keet, Maria
• d’Amato, Claudia
• Hogan, Aidan
• Takeda, Hideaki
• Monnin, Pierre

Titel

Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

Ist Teil von

• Lecture notes in computer science, 2022, p.575-591

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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/