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CMAI 2021 : 3rd International Workshop on Conceptual Modeling Meets Artificial Intelligence

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Link: https://workshop-cmai.github.io/2021/
 
When Oct 18, 2021 - Oct 21, 2021
Where St. John's, NL, Canada
Submission Deadline Jul 9, 2021
Notification Due Aug 6, 2021
Final Version Due Aug 20, 2021
Categories    conceptual modelling   artificial intelligence (ai)   explainable ai (xai)   model generation
 

Call For Papers

Co-Located with the 40th International Conference on Conceptual Modeling (ER 2021), 18-21 October 2021 St. John's, Canada: https://er2021.org

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
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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

Workshop Organizers
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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

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