K-iL2021: Workshop on Knowledge-infused Learning At the 2021 Knowledge Graph Conference |
Website | https://aiisc.ai/KiL2021/ |
Submission link | https://easychair.org/conferences/?conf=kil2021 |
K-iL2021 invites researchers and practitioners from both academia and industry who are interested in infusion of knowledge into Deep Learning, human-allied probabilistic learning, knowledge graph (KG) enhance natural language processing tasks, K-iL enhanced conversational agents (chatbots), interpretability and explainability, commonsense reasoning using KGs, KGs enable reinforcement learning agents, language models for KG representation, and KG enhance optimization for a wide array of real world applications (e.g. Social Good, Finance, Education, Healthcare)
Given the current pandemic situation due to the COVID-19 outbreak, we will be holding K-iL in full alignment with the decision of KGC2021 chairs, which is completely virtual, for both presenters and attendees. We will make certain the quality of the virtual workshop experience through appropriate technologies.
Submission Guidelines
We solicit the submission of papers in the following four categories:
- Regular research papers (9 pages including references) include recent or ongoing research
- Short papers (6 pages including references)
Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this workshop. All submissions should be double-blind and use CEUR-WS Latex Template for formatting. A high-resolution PDF of the paper should be uploaded to the EasyChair submission site before the paper submission deadline.
All papers will be peer-reviewed and single-blinded. Authors of Accepted papers will have the opportunity to present, will be published online and will not be considered archival.
Important Dates:
- Paper Submission: April 01, 2021 (23:59, anywhere on earth).
- Author Notification: April 20, 2021
- Camera-ready Papers Due: April 28, 2021
- Workshop Day: May 04, 2021
Topics
Topics for research and discussions include (but not limited to):
- Shallow, semi-deep or deep infusion of Knowledge into Deep Learning
- Human-allied Probabilistic Learning
- Knowledge graph enhanced Natural language Processing tasks such as Question Answering, and Natural Language Inference.
- K-iL enhanced Virtual Assistants/Conversational Systems, Human-Computer Interaction
- Knowledge Graph enhanced optimization for real-world applications (e.g. Social Good, Finance, Education, Healthcare)
- Interpretability and explainability afforded by K-iL
- Languages and models for knowledge graph representation
- Knowledge graph enabled Reinforcement Learning agents
- Commonsense Reasoning using Knowledge Graphs
Committees
Organizing Committee
- Amit Sheth, AI Institute, University of South Carolina, USA
- Ying Ding, Bill & Lewis Suit Professor, School of Information, University of Texas at Austin
- Pavan Kapanipathi, Research Staff Member, Reasoning Team, IBM Research
- Manas Gaur, AI Institute, University of South Carolina, USA
Venue
The conference will be held physically and virtually whose details will be updated soon.
Contact
Please email all inquiries about submissions to mgaur@email.sc.edu