Workshop at Knowledge Representation and Reasoning (KR2023) September 2-8, 2023
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.
Keywords fall into two groups; one group concerns the methodology, the other relates to data.
Models: Bayesian nets, Markov nets, credal nets, Dung-style argumentation, knowledge graphs, ontologies, data visualization
Data: 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.
Giuseppe Primiero (Milan)
Sadok Ben Yahia (Tallinn)
Deadline for paper submission: June 8, 2023.
Notification of authors: July 4, 2023.
We are calling for short papers (2 to 6 pages, LNCS-style) and long papers (up to 12 pages, LNCS-style).
Submissions via EasyChair. Make sure you select the track: Graphical Reasoning with Imperfect Data.
Accepted papers (short and long) will be published in an online non-archival format.
Leila Amgoud (CNRS, Université Toulouse 3)
Martin Adamčík (Assumption University)
Paolo Baldi (University of Milan)
Sadok Ben Yahia (Tallinn University of Technology)
Salem Benferhat (Université d’Artois)
Meghyn Bienvenu (CNRS, Université de Bordeaux)
Richard Booth (Cardiff University)
Camille Bourgaux (Ecole Normale Supérieure)
Katarina Britz (Stellenbosch University)
Giovanni Casini (ISTI-CNR)
Esther Anna Corsi (University of Milan)
Madalina Croitoru (Université de Montpellier)
Glauber De Bona (Universidade de São Paulo)
Francesco De Pretis (University of Modena and Reggio Emilia)
Andreas Herzig (CNRS, Université Toulouse 3)
Saïd Jabbour (Université d’Artois)
Mardi Jankowitz (University of South Africa)
Souhila Kaci (Université de Montpellier)
Myriam Lamolle (Université Paris 8)
Marie-Jeanne Lesot (Sorbonne Université)
Quentin Manière (Universität Leipzig)
Nédra Mellouli (Université Paris 8)
Tommie Meyer (University of Cape Town)
Petrus Potgieter (University of South Africa)
Soroush Rafiee Rad (Universiteit van Amsterdam)
Adrien Revault d’Allonnes (Université Paris 8)
Kai Sauerwald (FernUniversität Hagen)
Karima Sedki (Université Paris 13)
Tom Sterkenburg (LMU Munich)
Chris Swanepoel (University of South Africa)
Karim Tabia (Université d’Artois)
Matthias Thimm (FernUniversität Hagen)
Ivan Varzinczak (Université Paris 8)
Srdjan Vesic (CNRS, Université d’Artois)