Nov 15, 2021 · This paper proposes a novel system, namely OntoAugment, that augments LF labels for the ontology matching problem, starting from outcomes of the LFs.
PDF | On Nov 15, 2021, Fabio Maresca and others published OntoAugment: Ontology Matching through Weakly-Supervised Label Augmentation | Find, read and cite ...
Nov 15, 2021 · Ontology matching enables harmonizing heterogeneous data mod- els. Existing ontology matching approaches include machine learn- ing. In ...
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Inproceedings,. OntoAugment: Ontology Matching through Weakly-Supervised Label Augmentation. F. Maresca, G. Solmaz, and ...
OntoAugment: Ontology Matching through Weakly-Supervised Label Augmentation. F Maresca, G Solmaz, F Cirillo. Proceedings of the 19th ACM Conference on Embedded ...
OntoAugment: Ontology Matching through Weakly-Supervised Label Augmentation. F Maresca, G Solmaz, F Cirillo. Proceedings of the 19th ACM Conference on Embedded ...
This paper proposes a new approach called reinforced labeling (RL), given an unlabeled dataset and a set of LFs, that augments the LFs' outputs to cases not ...
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This paper proposes a semi-supervised learning approach for ontology matching that needs a small set of training examples, and exploits the dominant ...
Apr 11, 2024 · OntoAugment: Ontology Matching through Weakly-Supervised Label Augmentation. In Proceedings of the 19th ACM Conference on Embedded Networked ...