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The Impact of Imbalanced Training Data on Local Matching Learning of Ontologies
- Source :
- Business Information Systems ISBN: 9783030204846, BIS (1), Proceedings of BIS 2019, 22nd International Conference on Business Information Systems (BIS 2019), 22nd International Conference on Business Information Systems (BIS 2019), Jun 2019, Seville, Spain. pp.162-175, HAL
- Publication Year :
- 2019
- Publisher :
- Springer International Publishing, 2019.
-
Abstract
- International audience; Matching learning corresponds to the combination of ontology matching and machine learning techniques. This strategy has gained increasing attention in recent years. However, state-of-the-art approaches implementing matching learning strategies are not well-tailored to deal with imbalanced training sets. In this paper, we address the problem of the imbalanced training sets and their impacts on the performance of the matching learning in the context of aligning biomedical ontologies. Our approach is applied to local matching learning, which is a technique used to divide a large ontology matching task into a set of distinct local sub-matching tasks. A local matching task is based on a local classifier built using its balanced local training set. Thus, local classifiers discover the alignment of the local sub-matching tasks. To validate our approach, we propose an experimental study to analyze the impact of applying conventional resampling techniques on the quality of the local matching learning.
- Subjects :
- Ontology matching
Computer science
02 engineering and technology
Machine learning
computer.software_genre
Open Biomedical Ontologies
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
020204 information systems
Resampling
0202 electrical engineering, electronic engineering, information engineering
Semantic Web
Local matching
Training set
business.industry
[INFO.INFO-WB]Computer Science [cs]/Web
Imbalanced data set
Apprentissage
Web
020201 artificial intelligence & image processing
Artificial intelligence
business
Classifier (UML)
Ontology alignment
computer
Semantic web
Subjects
Details
- ISBN :
- 978-3-030-20484-6
- ISBNs :
- 9783030204846
- Database :
- OpenAIRE
- Journal :
- Business Information Systems ISBN: 9783030204846, BIS (1), Proceedings of BIS 2019, 22nd International Conference on Business Information Systems (BIS 2019), 22nd International Conference on Business Information Systems (BIS 2019), Jun 2019, Seville, Spain. pp.162-175, HAL
- Accession number :
- edsair.doi.dedup.....d9bbc43e2e840224d83f2c0817782aa1
- Full Text :
- https://doi.org/10.1007/978-3-030-20485-3_13