posted by organizer: aaaimake || 9471 views || tracked by 16 users: [display]

AAAI-MAKE 2021 : AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering

FacebookTwitterLinkedInGoogle

Link: https://www.aaai-make.info/
 
When Mar 22, 2021 - Mar 24, 2021
Where Stanford University, Palo Alto, CA, USA
Submission Deadline Nov 30, 2020
Notification Due Jan 10, 2021
Final Version Due Feb 19, 2021
Categories    artificial intelligence   machine learning   knowledge engineering   hybrid ai
 

Call For Papers

AAAI-MAKE 2021 brings together practitioners and researchers from various companies, research centers, and academia of machine learning and knowledge engineering working together on joint AI that is being explainable and grounded in domain knowledge.

Topics

• Enterprise AI
• Machine Learning
• Knowledge Engineering and Management
• Knowledge Representation and Reasoning
• Hybrid AI
• Explainable AI
• Conversational AI
• Deep Learning and Neural Networks
• Rule-based Systems
• Recommender Systems
• Scene Interpretation Systems
• Ontologies and Semantic Web
• Data Science

Use cases, application scenarios, and requirements from the industry would be highly beneficial and most welcome. Since AAAI-MAKE is a dedicated symposium on the combination of machine learning and knowledge engineering, the contributions shall address both AI domains. For more details, see the following webpage: https://www.aaai-make.info.

Submission

We solicit position/full papers (5 to 12 pages) and short papers (2 to 4 pages) that can include recent or ongoing research, business cases, application scenarios, and surveys. Furthermore, proposals for industrial side-tutorial events, demonstrations, and (panel) discussions are very welcome too.

Please submit through EasyChair (https://easychair.org/conferences/?conf=sss21).

All submissions must reflect the formatting instructions (https://www.aaai-make.info/authors) and will be reviewed by the program committee. Accepted and camera-ready papers shall be published on the established open-access proceedings site CEUR-WS.

Important Dates

• Extended submission due: 30th of November 2020
• Notification of authors: 10th of January 2021
• Registration: 12th of February 2021
• Camera-ready: 19th of February 2021
• Symposium: 22-24 of March 2021

Organizing Committee

• Hans-Georg Fill, University of Fribourg, Switzerland.
• Aurona Gerber, University of Pretoria, South Africa.
• Knut Hinkelmann (co-chair), FHNW University of Applied Sciences and Arts Northwestern Switzerland.
• Doug Lenat, Cycorp, Inc., Austin, TX, USA.
• Andreas Martin (co-chair), FHNW University of Applied Sciences and Arts Northwestern Switzerland.
• Reinhard Stolle, Argo AI GmbH, München, Germany.
• Frank van Harmelen, VU University, Amsterdam, Netherlands.

Contact

AAAI-MAKE 2021 is part of the Spring Symposium Series of the Association for the Advancement of Artificial Intelligence (AAAI). For any inquiries, please contact mail@aaai-make.info.

Related Resources

IEEE-Ei/Scopus-ITCC 2025   2025 5th International Conference on Information Technology and Cloud Computing (ITCC 2025)-EI Compendex
AAAI 2024   The 38th Annual AAAI Conference on Artificial Intelligence
SPIE-Ei/Scopus-DMNLP 2025   2025 2nd International Conference on Data Mining and Natural Language Processing (DMNLP 2025)-EI Compendex&Scopus
AAAI 2025   The 39th Annual AAAI Conference on Artificial Intelligence
IEEE-Ei/Scopus-CNIOT 2025   2025 IEEE 6th International Conference on Computing, Networks and Internet of Things (CNIOT 2025) -EI Compendex
AAAI-MAKE 2024   AAAI 2024 Spring Symposium on Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge
ICSTTE 2025   2025 3rd International Conference on SmartRail, Traffic and Transportation Engineering (ICSTTE 2025)
AMLDS 2025   IEEE--2025 International Conference on Advanced Machine Learning and Data Science
21st AIAI 2025   21st (AIAI) Artificial Intelligence Applications and Innovations
Good-Data@AAAI 2025   AAAI 2025 Workshop on Preparing Good Data for Generative AI: Challenges and Approaches (Good-Data)