[PlanetKR] KR2022: Session on KR and Machine Learning

Jesse Heyninck jesse.heyninck at gmail.com
Sun Jan 9 10:19:25 UTC 2022


Call for Papers: Special Session on KR and Machine Learning

July 31 - August 5, 2022, Haifa, Israel


* Submission of title and abstract: February 2, 2022
* Paper submission deadline: February 9, 2022
* Author response period: March 29-31, 2022
* Author notification: April 15, 2022
* Camera-ready papers: May 7, 2022
* Conference: July 31 - August 5, 2022

The last few years have witnessed a growing interest in AI methods that
combine aspects of Machine Learning (ML) with insights and methods from the
field of Knowledge Representation and Reasoning (KR). This trend is
essentially motivated by the clear complementarity of ML and KR. For
instance, the popularity and success of ML based systems has put issues
such as explainability, bias and fairness firmly in the spotlight, and
addressing these issues naturally leads to systems in which symbolic (or at
least interpretable) representations play a more central role. On the other
hand, ML also offers solutions for long-standing challenges in the field of
KR, for instance related to efficient, noise-tolerant and ampliative
inference, knowledge acquisition, and the limitations of symbolic
representations. The synergy between ML and KR has the potential to lead to
new advancements in fundamental AI challenges including, but not limited
to, learning symbolic generalisations from raw (multi-modal) data, using
knowledge to facilitate data-efficient learning, supporting
interpretability of learned outcomes, federated multi-agent learning and

This year, for the third time, KR2022 will host a special session on
"Knowledge Representation and Machine Learning", which aims at providing
researchers and practitioners with a dedicated forum for the discussion of
new ideas and research results at the intersection of these two fields.
This special session will provide participants with the opportunity to make
meaningful connections and develop a shared understanding of the challenges
involved in developing innovative AI solutions that rely on a combination
of insights and methods from ML and KR.

The Special Session on KR and ML at KR2022 invites submissions of papers
that combine aspects of KR and ML research, including the use of KR methods
for solving ML challenges (e.g. knowledge-guided or explainable learning),
the use of ML methods for solving KR challenges (e.g. efficient inference,
knowledge base completion), the integration of learning and reasoning, and
the application of combined KR and ML approaches to solve real-world
We welcome papers on a wide range of topics, including but not limited to:
* Learning symbolic knowledge, such as ontologies and knowledge graphs,
action theories, commonsense knowledge, spatial and temporal theories,
preference models and causal models
* Logic-based, logical and relational learning algorithms
* Machine-learning driven reasoning algorithms
* Neural-symbolic learning
* Statistical relational learning
* Multi-agent learning
* Symbolic reinforcement learning
* Learning symbolic abstractions from unstructured data
* Explainable AI
* Expressive power of learning representations
* Knowledge-driven natural language understanding and dialogue
* Knowledge-driven decision making
* Knowledge-driven intelligent systems for internet of things and
* Architectures that combine data-driven techniques and formal reasoning

The Special Session on KR and Machine Learning will allow contributions of
both regular papers (9 pages) and short papers (4 pages), excluding
references, prepared and submitted according to the authors guidelines in
the submission page:


The special session welcomes contributions that extend the state-of-the-art
at the intersection of KR and ML. Therefore, KR-only or ML-only submissions
will not be accepted for evaluation in this special session.

Submissions will be rigorously peer reviewed by PC members who are active
in KR and ML. Submissions will be evaluated on the basis of the
originality, soundness, relevance and significance of the technical
contribution, as well as the overall presentation quality.

** CHAIRS **
Fabio Cozman University of Sao Paulo, Brazil
Steven Schockaert Cardiff University, UK
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