Analogical proportions, i.e., statements of the form "A is to B as C is to D", are the basis of analogical inference and they are closely related to case-based reasoning and transfer learning.
They are have been used on NLP tasks such as automatic machine translation, semantic and morphological tasks, as well as visual question answering with competitive results.
Moreover, analogical reasoning can support several machine learning tasks such as classification, decision making, or dataset augmentation.
However, other less explored applications could be envisioned such as knowledge discovery and management (e.g., knowledge graphs refinement, data set completion, and alignment), recommender systems, and other AI-related tasks such as explainable AI.

The purpose of this workshop is thus to explore both foundational and applicative aspects of analogical reasoning in various fields, e.g., machine learning, knowledge representation, discovery, and reasoning, as well as in industry practice with real-world data, applications, and associated challenges, for instance, scalability issues.


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Themes and topics
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We invite submissions of research papers on the foundational theory of analogies, on interactions between case-based reasoning and analogies, as well as on applicative use-case studies.
Topics of interest include, but are not limited to:

* Foundational theory of analogies:
- Axiomatic approaches to analogical proportions;
- Analogy-preserving functions;
- Interactions between case-based and analogical reasoning.

* Applications:
- Analogical reasoning in machine learning;
- Analogical reasoning in visual domains;
- Analogical reasoning in Natural Language Processing;
- Analogical reasoning in healthcare;
- Analogy based explanations;
- Analogies in software engineering;
- Analogies in knowledge management.


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Submission Guidelines
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Submitted papers must be formatted according to Springer LNCS formatting guidelines, see:
https://iccbr2022.loria.fr/call-for-papers/

We welcome contributions in the form of short papers (up to 6 pages including references) and long papers (up to 12 pages + references).
Submissions can describe either work in progress or mature work.

All papers will be thoroughly reviewed (single-blind).
Overlength and/or out-of-scope papers will be rejected without review.

Paper submission: Please use EasyChair at https://easychair.org/conferences/?conf=iccbr2022.
Make sure to select the track "Workshop on Analogies: from Theory to Applications".


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Proceedings
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All papers will appear in the pre-proceedings made available on the workshop webpage.
Original contributions will be published as proposed by ICCBR 2022.
In addition, authors of accepted papers will be invited to submit an extended version of their work to a special issue in Annals of Mathematics and Artificial Intelligence (AMAI-Springer).


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Chairs
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* Miguel Couceiro (LORIA, miguel.couceiro@loria.fr)
* Esteban Marquer (LORIA, esteban.marquer@loria.fr)
* Pierre Monnin (Orange, pierre.monnin@orange.com)
* Pierre-Alexandre Murena (Aalto University, pierre-alexandre.murena@aalto.fi)