37 lines
4.5 KiB
Markdown
37 lines
4.5 KiB
Markdown
date:: 2021
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publisher:: IEEE
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place:: "Fukuoka, Japan"
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conference-name:: 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)
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proceedings-title:: 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)
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isbn:: 978-1-66542-484-4
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title:: @Encoding Conceptual Models for Machine Learning: A Systematic Review
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item-type:: [[conferencePaper]]
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original-title:: Encoding Conceptual Models for Machine Learning: A Systematic Review
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language:: en
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library-catalog:: DOI.org (Crossref)
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links:: [Local library](zotero://select/library/items/YCK2H4FE), [Web library](https://www.zotero.org/users/1039502/items/YCK2H4FE)
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-
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- [[Abstract]]
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- Conceptual models are essential in Software and Information Systems Engineering to meet many purposes since they explicitly represent the subject domains. Machine Learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works encode models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models’ structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model’s structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.
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- [[Attachments]]
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- [Encoding Conceptual Models for Machine Learning: A Systematic Review](zotero://select/library/items/3EZ594JB) {{zotero-imported-file 3EZ594JB, "Encoding Conceptual Models for Machine Learning A Systematic Review.pdf"}}
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- [[Highlights]]
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- ((651a8452-9451-48da-b2c6-9d0ae7257ea7))
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-
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- date:: 2021
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publisher:: IEEE
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place:: "Fukuoka, Japan"
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conference-name:: 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)
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proceedings-title:: 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)
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isbn:: 978-1-66542-484-4
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title:: @Encoding Conceptual Models for Machine Learning: A Systematic Review
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item-type:: [[conferencePaper]]
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original-title:: Encoding Conceptual Models for Machine Learning: A Systematic Review
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language:: en
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library-catalog:: DOI.org (Crossref)
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links:: [Local library](zotero://select/library/items/YCK2H4FE), [Web library](https://www.zotero.org/users/1039502/items/YCK2H4FE)
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- [[Abstract]]
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- Conceptual models are essential in Software and Information Systems Engineering to meet many purposes since they explicitly represent the subject domains. Machine Learning (ML) approaches have recently been used in conceptual modeling to realize, among others, intelligent modeling assistance, model transformation, and metamodel classification. These works encode models in various ways, making the encoded models suitable for applying ML algorithms. The encodings capture the models’ structure and/or semantics, making this information available to the ML model during training. Therefore, the choice of the encoding for any ML-driven task is crucial for the ML model to learn the relevant contextual information. In this paper, we report findings from a systematic literature review which yields insights into the current research in machine learning for conceptual modeling (ML4CM). The review focuses on the various encodings used in existing ML4CM solutions and provides insights into i) which are the information sources, ii) how is the conceptual model’s structure and/or semantics encoded, iii) why is the model encoded, i.e., for which conceptual modeling task and, iv) which ML algorithms are applied. The results aim to structure the state of the art in encoding conceptual models for ML.
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- [[Attachments]]
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- [Encoding Conceptual Models for Machine Learning: A Systematic Review](zotero://select/library/items/3EZ594JB) {{zotero-imported-file 3EZ594JB, "Encoding Conceptual Models for Machine Learning A Systematic Review.pdf"}} |