Conference item
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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 357.7KB, Terms of use)
-
- Publisher copy:
- 10.1007/978-3-031-19433-7_33
Authors
- Publisher:
- Springer
- Host title:
- The Semantic Web – ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23–27, 2022, Proceedings
- Pages:
- 575–591
- Series:
- Lecture Notes in Computer Science
- Series number:
- 13489
- Publication date:
- 2022-10-16
- Acceptance date:
- 2022-07-22
- Event title:
- 21st International Semantic Web Conference (ISWC 2022)
- Event location:
- Hangzhou, China
- Event website:
- https://iswc2022.semanticweb.org/
- Event start date:
- 2022-10-23
- Event end date:
- 2022-10-27
- DOI:
- EISSN:
-
1611-3349
- ISSN:
-
0302-9743
- EISBN:
- 9783031194337
- ISBN:
- 9783031194320
- Language:
-
English
- Keywords:
- Pubs id:
-
1328779
- Local pid:
-
pubs:1328779
- Deposit date:
-
2023-02-17
Terms of use
- Copyright holder:
- He et al.
- Copyright date:
- 2022
- Rights statement:
- © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Notes:
-
This is the accepted manuscript version of the paper. The final version is available online from Springer at: http://dx.doi.org/10.1007/978-3-031-19433-7_33
This paper was nominated as a Best Resource Paper candidate at ISWC 2022.
If you are the owner of this record, you can report an update to it here: Report update to this record