CSKGs@AAAI-21: The AAAI-21 Workshop on Commonsense Knowledge Graphs |
Website | https://usc-isi-i2.github.io/AAAI21workshop/ |
Submission link | https://easychair.org/conferences/?conf=cskgsaaai21 |
Submission deadline | November 9, 2020 |
Commonsense knowledge graphs (CSKGs) are sources of background knowledge that are expected to contribute to downstream tasks like question answering, robot manipulation, and planning. The knowledge covered in CSKGs varies greatly, spanning procedural, conceptual, and syntactic knowledge, among others. CSKGs come in a wider variety of forms compared to traditional knowledge graphs, ranging from (semi-)structured knowledge graphs, such as ConceptNet, ATOMIC, and FrameNet, to the recent idea to use language models as knowledge graphs. As a consequence, traditional methods of integration and usage of knowledge graphs might need to be expanded when dealing with CSKGs. Understanding how to best integrate and represent CSKGs, leverage them on a downstream task, and tailor their knowledge to the particularities of the task, are open challenges today. The workshop on CSKGs addresses these challenges, by focusing on the creation of commonsense knowledge graphs and their usage on downstream commonsense reasoning tasks.
The workshop will consist of: (1) two keynote talks, (2) a panel discussion on ‘Are language models enough?’, (3) presentations of full, short, and position papers, and (4) a discussion session.
Submission Guidelines
We welcome submissions of long (max. 8 pages), short (max. 4 pages), and position (max. 4 pages) papers describing new, previously unpublished research in this field. The page limits are including the references. Submissions must be formatted in the AAAI submission format.
All submissions should be done electronically via EasyChair: https://easychair.org/conferences/?conf=cskgsaaai21.
List of Topics
- Creation/extraction of new CSKGs
- Integration of existing CSKGs
- Exploration of CSKGs
- Impact of CSKGs on downstream tasks
- Methods of including CSKG knowledge in downstream tasks
- Probing for knowledge needs in downstream tasks
- Evaluation data/metrics relevant for CSKGs
- Identifying and/or filling gaps in CSKGs
Committees
Program Committee
- Chandra Bhagavatula (Allen Institute for AI, Washington, USA)
- Michele Catasta (Stanford University, California, USA)
- Marjorie Friedman (USC Information Sciences Institute, Massachusetts, USA)
- Aldo Gangemi (University of Bologna and ISTC, National Research Council, Italy)
- Henry Lieberman (MIT, Massachusetts, USA)
- Roberto Navigli (Sapienza University of Rome, Italy)
- Valentina Presutti (ISTC, National Research Council, Italy)
- Simon Razniewski (Max Planck Institute for Informatics, Germany)
- German Rigau (University of the Basque Country, Spain)
- Daniel Schwabe (Pontificia Universidade Católica (PUC), Rio de Janeiro, Brazil)
- Niket Tandon (Allen Institute for AI, Washington, USA)
- Piek Vossen (VU Amsterdam, The Netherlands)
Organizing committee
- Filip Ilievski (Information Sciences Institute, University of Southern California; ilievski@isi.edu)
- Alessandro Oltramari (Bosch Research and Technology Center, Pittsburgh; Alessandro.Oltramari@us.bosch.com)
- Deborah McGuinness (Rensselaer Polytechnic Institute; dlm@cs.rpi.edu)
- Pedro Szekely (Information Sciences Institute, University of Southern California; szekely@usc.edu )
Invited Speakers
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Yejin Choi (Brett Helsel Associate Professor, Paul G. Allen School of Computer Science & Engineering, University of Washington and Allen Institute for Artificial Intelligence)
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Joshua Tenenbaum (Professor of Cognitive Science and Computation at the Massachusetts Institute of Technology)
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David Ferrucci (Founder and CEO Elemental Cognition, Director of Applied AI, Bridgewater Associates, ex. IBM Watson)
- Shih-Fu Chang (Richard Dicker Professor and senior executive vice dean of the School of Engineering and Applied Science of Columbia University)
Venue
The conference will be held virtually, and co-located with AAAI'21.
Contact
All questions about submissions should be emailed to Filip Ilievski <ilievski@isi.edu>.