[DL] 2nd Call for papers: GRID Workshop @ KR2023

[Sihem Belabbes]

** Apologies for multiple postings ** 

CALL FOR PAPERS 

The First International Workshop on Graphical Reasoning with Imperfect
Data, collocated with KR2023 (https://imsva91-ctp.trendmicro.com:443/wis/clicktime/v1/query?url=https%3a%2f%2fkr.org%2fKR2023%2f&umid=F5768326-FBCE-7D05-88AF-9AFE4B0F0CEF&auth=477d61ecbd003aae820a8a1727e558b61618995e-baca5d670d3dfb8276333166b8b0df9e8191084c).
Website: GRID2023 [1] 

INTRODUCTION 

The quality of real-world data is a major issue in many application
domains, especially in data-intensive AI applications. As a matter of
fact, the presence of inconsistent, incomplete, inaccurate or uncertain
data may not only hamper problem modeling and solving, but also the
process of explaining the solutions. More theoretical approaches, on the
other hand, have provided graphical tools for principled knowledge
representation and reasoning that are much more transparent than opaque
real-world AI applications. Indeed, graphical models such as Bayesian
networks, knowledge graphs and ontologies have long been used to
facilitate the representation of and the reasoning with imperfect data.
By providing a visual dimension, they contribute to increasing the
understanding of the problem, and thus help practitioners, users (lay
and expert) as well as engineers in designing solutions that improve
trust, efficiency and transparency. 

The GRID workshop aims at bringing the applied and the theoretical
approaches together for the benefit of both. Real-world applications can
gain in explainability through the use of graphical models, while
addressing the intrinsic challenges of poor quality data can improve the
understanding and use of graphical models. 

Small workshops are often organised around one unifying theme, be it a
methodological theme or a challenge. We here focus on both a
methodological theme and a challenge. Our approach will bring together
1) a variety of methods drawing on graphical representation and 2) a
variety of application domains dealing with different aspects of
imperfect data. The varieties generate the necessary distance between
approaches to provide opportunities for cross-pollination, while
similarities of methods and domains make for a sufficient closeness
guaranteeing mutual relevance. 

TOPICS 

This workshop welcomes submissions dealing with the general topics of
graphical models and imperfect data and their integration, from the
perspective of knowledge representation and reasoning. A non-exhaustive
list of keywords includes: 

+ Bayesian nets
+ Markov nets
+ Credal nets
+ Dung-style argumentation
+ Knowledge graphs
+ Ontologies
+ Data visualization tools
+ Inconsistent data
+ Uncertain data
+ Data integration
+ Multi-source data
+ Heterogeneous data
+ Consistency maintenance
+ Real-world data
+ Data imputation
+ Missing data
+ Spurious data
+ Data loss
+ Outliers
+ Adversarial data 

SUBMISSION GUIDELINES 

Papers should be formatted according to the LNCS style and submitted as
a single PDF file. We welcome submissions of theoretical and practical
work including early-stage research ideas, methods, tools and
applications. We welcome short papers (2-6 pages), and long papers (up
to 12 pages). All accepted papers will be considered for an oral
presentation. The reviewing process will be light and driven more by
relevance to the workshop and the paper's potential to foster discussion
than by the technical maturity of the contribution. Workshop submissions
and camera-ready versions will be handled by EasyChair. 

Please submit at: https://easychair.org/my/conference?conf=grid2023

IMPORTANT DATES 

- May 31, 2023: Submission deadline
- July 4, 2023: Paper notification
- July 31, 2023: Camera-ready papers due 

PROCEEDINGS 

The accepted papers will appear on the workshop website and the online
proceedings will be non-archival. The copyright will remain with the
authors.

ORGANIZING COMMITTEE 

Sihem Belabbes, LIASD, IUT de Montreuil, Université Paris 8, France.
Jürgen Landes, University of Milan, Italy. 

PROGRAM COMMITTEE 

Leila Amgoud (CNRS, Université Toulouse 3, FR),
Martin Adamčík (Assumption University, TH),
Paolo Baldi (University of Milan, IT),
Sadok Ben Yahia (Tallinn University of Technology, EE),
Salem Benferhat (Université d'Artois, FR),
Meghyn Bienvenu, (CNRS, Université de Bordeaux, FR),
Richard Booth (Cardiff University, UK),
Camille Bourgaux (Ecole Normale Supérieure, FR),
Katarina Britz (Stellenbosch University, SA),
Giovanni Casini (ISTI-CNR, IT),
Esther Anna Corsi (University of Milan, IT),
Madalina Croitoru (Université de Montpellier, FR),
Glauber De Bona (Universidade de São Paulo, BR),
Francesco De Pretis (University of Modena and Reggio Emilia, IT),
Andreas Herzig (CNRS, Université Toulouse 3, FR),
Sa ̈ıd Jabbour (Université d'Artois, FR),
Mardi Jankowitz (University of South Africa, SA),
Souhila Kaci (Université de Montpellier, FR),
Myriam Lamolle (Université Paris 8, FR),
Marie-Jeanne Lesot (Sorbonne Université, FR),
Quentin Manière (Universität Leipzig, DE),
Nédra Mellouli (Université Paris 8, FR),
Tommie Meyer (University of Cape Town, SA),
Petrus Potgieter (University of South Africa, SA),
Soroush Rafiee Rad (ILLC Universiteit van Amsterdam, NL),
Adrien Revault d'Allonnes (Université Paris 8, FR),
Kai Sauerwald (FernUniversität in Hagen, DE),
Karima Sedki (Université Paris 13, FR),
Tom Sterkenburg (Ludwig-Maximilians-Universität Munich, DE),
Chris Swanepoel (University of South Africa, SA),
Karim Tabia (Université d'Artois, FR),
Matthias Thimm (FernUniversität in Hagen, DE),
Ivan Varzinczak (Université Paris 8, FR),
Srdjan Vesic (CNRS, Université d'Artois, FR). 

CONTACT 

belabbes at 1    and    juergen_landes at 2 

1: iut.univ-paris8.fr
2: yahoo.de 

Links:
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[1]
https://jlandes.wordpress.com/graphical-reasoning-with-imperfect-data-grid-september-2-4-2023/
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