Augmentation of Semantic Processes for Deep Learning Applications.
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| Title: | Augmentation of Semantic Processes for Deep Learning Applications. |
|---|---|
| Authors: | Hoffmann, Maximilian1,2 (AUTHOR) hoffmannm@uni-trier.de, Malburg, Lukas1,2 (AUTHOR), Bergmann, Ralph1,2 (AUTHOR) |
| Source: | Applied Artificial Intelligence. Dec2025, Vol. 39 Issue 1, p1-48. 48p. |
| Subjects: | Deep learning, Manufacturing process management, Knowledge transfer, Pattern recognition systems, Data quality, Supervised learning, Process optimization |
| Abstract: | The popularity of Deep Learning (DL) methods used in business process management research and practice is constantly increasing. One important factor that hinders the adoption of DL in certain areas is the availability of sufficiently large training datasets, particularly affecting domains where process models are mainly defined manually with a high knowledge-acquisition effort. In this paper, we examine process model augmentation in combination with semi-supervised transfer learning to enlarge existing datasets and train DL models effectively. The use case of similarity learning between manufacturing process models is discussed. Based on a literature study of existing augmentation techniques, a concept is presented with different categories of augmentation from knowledge-light approaches to knowledge-intensive ones, e. g. based on automated planning. Specifically, the impacts of augmentation approaches on the syntactic and semantic correctness of the augmented process models are considered. The concept also proposes a semi-supervised transfer learning approach to integrate augmented and non-augmented process model datasets in a two-phased training procedure. The experimental evaluation investigates augmented process model datasets regarding their quality for model training in the context of similarity learning between manufacturing process models. The results indicate a large potential with a reduction of the prediction error of up to 53%. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189934089 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Augmentation of Semantic Processes for Deep Learning Applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hoffmann%2C+Maximilian%22">Hoffmann, Maximilian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> hoffmannm@uni-trier.de</i><br /><searchLink fieldCode="AR" term="%22Malburg%2C+Lukas%22">Malburg, Lukas</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bergmann%2C+Ralph%22">Bergmann, Ralph</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Artificial+Intelligence%22">Applied Artificial Intelligence</searchLink>. Dec2025, Vol. 39 Issue 1, p1-48. 48p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+process+management%22">Manufacturing process management</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The popularity of Deep Learning (DL) methods used in business process management research and practice is constantly increasing. One important factor that hinders the adoption of DL in certain areas is the availability of sufficiently large training datasets, particularly affecting domains where process models are mainly defined manually with a high knowledge-acquisition effort. In this paper, we examine process model augmentation in combination with semi-supervised transfer learning to enlarge existing datasets and train DL models effectively. The use case of similarity learning between manufacturing process models is discussed. Based on a literature study of existing augmentation techniques, a concept is presented with different categories of augmentation from knowledge-light approaches to knowledge-intensive ones, e. g. based on automated planning. Specifically, the impacts of augmentation approaches on the syntactic and semantic correctness of the augmented process models are considered. The concept also proposes a semi-supervised transfer learning approach to integrate augmented and non-augmented process model datasets in a two-phased training procedure. The experimental evaluation investigates augmented process model datasets regarding their quality for model training in the context of similarity learning between manufacturing process models. The results indicate a large potential with a reduction of the prediction error of up to 53%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/08839514.2025.2506788 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 48 StartPage: 1 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Manufacturing process management Type: general – SubjectFull: Knowledge transfer Type: general – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Data quality Type: general – SubjectFull: Supervised learning Type: general – SubjectFull: Process optimization Type: general Titles: – TitleFull: Augmentation of Semantic Processes for Deep Learning Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hoffmann, Maximilian – PersonEntity: Name: NameFull: Malburg, Lukas – PersonEntity: Name: NameFull: Bergmann, Ralph IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08839514 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Applied Artificial Intelligence Type: main |
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