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

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...

Full description

Saved in:
Bibliographic Details
Published inThe Semantic Web - ISWC 2022 Vol. 13489; pp. 575 - 591
Main Authors He, Yuan, Chen, Jiaoyan, Dong, Hang, Jiménez-Ruiz, Ernesto, Hadian, Ali, Horrocks, Ian
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2022
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Biomedical ontology
Equivalence matching
Evaluation resource
OAEI
Ontology Alignment
Subsumption matching
Online AccessGet full text
ISBN9783031194320
3031194322
ISSN0302-9743
1611-3349
DOI10.1007/978-3-031-19433-7_33

Cover

More Information
Summary: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/
ISBN:9783031194320
3031194322
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-031-19433-7_33