Special Session:
Knowledge Representation meets Machine Learning (KRxML)
Combining aspects of knowledge representation (KR) and machine learning (ML) has received a great deal of attention in recent years. This trend is motivated by the clear complementary of KR and ML. For instance, ML-based systems have brought issues such as explainability, bias, fairness, sustainability, and symbol grounding into the spotlight. Addressing these issues naturally leads to systems that emphasize symbolic representations. On the other hand, ML provides solutions to long-standing challenges in KR, such as, efficient and noise-tolerant inference, automatic knowledge acquisition, and the limitations of symbolic representations.
The combination of KR and ML has potential that leads to new advancements in fundamental AI challenges including, but not limited to, Fairness, Accountability and Transparency AI (FAccT AI), using knowledge to facilitate data-efficient learning, supporting interpretability of learned outcomes, learning symbolic generalization from raw data, etc.
This special session will bring together experts from academia and industry across different countries to discuss new ideas and results at the intersection of these two research fields. It is expected to provide participants with the opportunity to make meaningful connections and develop mutual understanding using a combination of insights and methods from ML and KR.
Expected Contributions The special session “KRxML” at KSE 2023 invites submissions of papers that combines aspects of KR and ML research. Potential topics include, but not limited to:
- Learning symbolic knowledge base, such as ontologies and knowledge graphs
- Knowledge representation learning, such as knowledge graph embedding
- Neural-symbolic learning
- Knowledge-driven reinforcement learning
- Knowledge-guided machine learning
- Explainable learning
- Argumentation and explainability
- Argument mining from raw (mult-modal) data
- KRxML for FAccT AI
- Architectures that combine data-driven techniques and formal reasoning
- Applications that combine KR and ML to solve real-world problems.
Paper Submission The papers of this session will be printed in the proceedings of the main conference, which will be published by IEEE and be available at the conference. Papers should be submitted through the online paper submission system.
The authors are invited to submit their full papers by the deadline through the KSE 2023 submission page http://www.easychair.org/conferences/?conf=kse2023. The submissions will be peer-reviewed for originality and scientific quality. The proceedings will be published by IEEE (pending) and be available at the conference. Papers should follow the LaTeX series format as described on IEEE’s website (http://www.ieee.org/conferences_events/conferences/publishing/templates.html) and should not exceed 6 pages. Select Session: Knowledge Representation meets Machine Learning (KRxML)
Important Dates The submission deadline is 23:59 AOE (Anywhere on Earth, and will be adjusted with the main conference accordingly).
- Paper submission: June 15th, 2023
- Notification date: July 30th, 2023
- Final camera-ready: September 24th, 2023
Session Organizers
- Teeradaj Racharak, Japan Advanced Institute of Science and Technology, Japan
- Akkharawoot Takhom, Thammasat University, Thailand
PC Members (sorted by country)
- Andreas Pester, The British University in Egypt, Egypt
- Danial Hooshyar, Tallinn University, Estonia
- Prarinya Siritanawan, Japan Advanced Institute of Science and Technology, Japan
- Natthawut Kertkeidkachorn, Japan Advanced Institute of Science and Technology, Japan
- Satoshi Tojo, Japan Advanced Institute of Science and Technology, Japan
- Frederic Andres, National Institute of Informatics, Japan
- Nguyen Duy Hung, Thammasat University, Thailand
- Peerasak Intarapaiboon, Independent Researcher, Thailand
- Watanee Jearanaiwongkul, Independent Researcher, Thailand
- Jiraporn Pooksook, Naresuan University, Thailand
- Taneth Ruangrajitpakorn, Thailand’s National Electronics and Computer Technology Center, Thailand
Contact
- Teeradaj Racharak: racharak[atmark]jaist.ac.jp
- Akkharawoot Takhom: takkhara[atmark]tu.ac.th
|