[AISWorld] CfP: 3rd Int. Workshop on Conceptual Modeling Meets Artificial Intelligence (CMAI 2021) at ER'2021
Bork, Dominik
dominik.bork at tuwien.ac.at
Tue Apr 6 09:34:31 EDT 2021
We kindly invite you to submit your research to the
3rd Int. Workshop on Conceptual Modeling Meets Artificial Intelligence
(CMAI 2021)
Co-Located with the 40th International Conference on Conceptual Modeling
(ER 2021), 18-21 October 2021 St. Johns, Canada: https://er2021.org
See full version at: https://workshop-cmai.github.io/2021/
Call for Papers
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Artificial intelligence (AI) is front and center in the data-driven
revolution that has been taking place in the last couple of years with
the increasing availability of large amounts of data (“big data”) in
virtually every domain. The now dominant paradigm of data-driven AI,
powered by sophisticated machine learning algorithms, employs big data
to build intelligent applications and support fact-based decision
making. The focus of data-driven AI is on learning (domain) models and
keeping those models up-to-date by using statistical methods over big
data, in contrast to the manual modeling approach prevalent in
traditional, knowledge-based AI.
While data-driven AI has led to significant breakthroughs, it also comes
with a number of disadvantages. First, models generated by machine
learning algorithms often cannot be inspected and understood by a human
being, thus lacking explainability. Furthermore, integration of
preexisting domain knowledge into learned models – prior to or after
learning – is difficult. Finally, correct application of data-driven AI
depends on the domain, problem, and organizational context while
considering human aspects as well. Conceptual modeling can be the key to
applying data-driven AI in a meaningful, correct, and time-efficient way
while improving maintainability, usability, and explainability.
Topics of Interest
-----------------
The topics of interest include, but are not limited to, the following:
- Combining generated and manually engineered models
- Combining symbolic with sub-symbolic models
- Conceptual (meta-)models as background knowledge for model learning
- Conceptual models for enabling explainability, model validation and
plausibility checking
- Trade-off between interpretability and model performance
- Reasoning in generated models
- Data-driven modeling support
- Learning of meta-models
- Automatic, incremental model adaptation
- Case-based reasoning in the context of model generation and conceptual
modeling
- Model-driven guidance and support for data analytics lifecycle
- Conceptual models for supporting users with conducting data analysis
Important Dates
--------------
Paper submission: 16 June 2021
Author notification: 15 July 2021
Camera-ready Version: 29 July 2021
Workshop Organizers
------------------
Dominik Bork, TU Wien, Austria
Peter Fettke, German Research Center for Artificial Intelligence,
Saarland University, Germany
Ulrich Reimer, Eastern Switzerland University of Applied Sciences,
Switzerland
Marina Tropmann-Frick, University of Applied Sciences Hamburg, Germany
--
Ass. Prof. Dr. Dominik Bork
Assistant Professor for Business Systems Engineering
Business Informatics Group (BIG)
Institute of Information Systems Engineering
TU Wien
Favoritenstr. 9-11 / 194-3
Stiege 2, 2. Stock, Raum HG0206
1040 Wien, Österreich
Tel: +43 (1) 58801-194308
Web: https://model-engineering.info/
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