Aleksandar Gavric


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Univ.Ass. MEng. B.Eng.

Aleksandar Gavric

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  • Roles: PreDoc Researcher

Publications

Encoding Conceptual Models for Machine Learning: A Systematic Review
Syed Juned AliAleksandar GavricHenderik ProperDominik Bork

View .bib

Handle: 20.500.12708/193226; Year: 2023; Issued On: 2023-01-01; Type: Publication; Subtype: Inproceedings; Peer Reviewed:

Keywords: Conceptual Modeling, Analytical models, Systematics, Machine Learning algorithms, Bibliographies, Semantics, Machine Learning
Astract: 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 en-code 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.

Ali, S. J., Gavric, A., Proper, H., & Bork, D. (2023). Encoding Conceptual Models for Machine Learning: A Systematic Review. In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (pp. 562–570). IEEE. https://doi.org/10.1109/MODELS-C59198.2023.00094


Teaching

Information Systems Engineering
Semester: 2024W; Nr: 194.143; Type: VU; Hours: 4.0; Language: English; View on TISS

Team

Business Informatics Group, TU Wien

Head


Team member

Henderik Proper

Univ.Prof. PhD

Professors


Team member

Dominik Bork

Associate Prof. Dipl.-Wirtsch.Inf.Univ.
Dr.rer.pol.

Team member

Christian Huemer

Ao.Univ.Prof. Mag.rer.soc.oec.
Dr.rer.soc.oec.

Team member

Gerti Kappel

O.Univ.Prof.in Dipl.-Ing.in
Mag.a Dr.in techn.

Team member

Henderik Proper

Univ.Prof. PhD

Visiting Scientists


Team member

Christiane Floyd

Hon.Prof.in Dr.in phil.

Team member

Johanna Barzen

Dr. phil.

Administration



Researchers


Team member

Syed Juned Ali

Univ.Ass. BSc MSc

Team member

Aleksandar Gavric

Univ.Ass. MEng. B.Eng.

Team member

Marion Murzek

Senior Lecturer Mag.a rer.soc.oec.
Dr.in rer.soc.oec.

Team member

Galina Paskaleva

Projektass.in Dipl.-Ing.in
Dipl.-Ing.in BSc

Team member

Marianne Schnellmann

Univ.Ass.in BSc MSc

Team member

Marion Scholz

Senior Lecturer Dipl.-Ing.in
Mag.a rer.soc.oec.

External Researchers


Team member

Markus Hafner

Gastwissenschaftl. MEng.



Team member

Marco Huymajer

Univ.Ass. Dipl.-Ing.