This volume contains the papers presented at the Sixteenth International Conference on Concept Lattices and Their Applications, CLA 2022, including the satellite workshop entitled “Existing Tools and The CLA conference is an international forum for researchers, practitioners and students dedicated to the practice of Formal Concept Analysis (FCA) and areas closely related to it, including data analysis and mining, information retrieval, knowledge management, knowledge engineering, logic, algebra and lattice theory.
Belgium) and illustrate the use of formal conceptual analysis with examples of concrete datasets. This workshop was organized by Alexandre Bazin, Karell Bertet, Christophe Demko, Pierre Martin and Ants Torim, and, after peer review, six short papers have been accepted for ETAFCA’2022. This volume includes all of these contributions.
This edition of CLA also includes a sponsored tutorial entitled “Managing and Linking Data with KG Using PP Semantic Suite” which was given by Albin Ahmeti and Artem Revenko, a tutorial entitled “FCA algorithms with the R package fcaR” by Domingo Lopez and Angel Mora, and another one entitled “Reduction of fuzzy contexts” by Jan Konecny.
We would like to thank all authors and speakers for their extra efort to provide high quality contributions, and the program committee members and external reviewers for their careful and timely review of the submissions. We would also like to thank the CLA steering committee for the opportunity to contribute to the advancement of FCA and related areas. And a special thanks to the local committee. We are all indebted to you.
We would like to thank our sponsors, namely Semantic Web Company and Tallinn University. Finally, we also do not forget that the conference was managed (quite easily) with the Easychair system, for many tasks including paper submission, selection, and reviewing.
CLA 2022 was organized by Tallinn University of Technology.
Sadok Ben Yahia Jean Diatta Peter Eklund Sergei O. Kuznetsov
Tallinn University of Technology, Estonia Université de la Réunion, France IT University of Copenhagen, Denmark National Research University Higher School of Economics, Moscow, Russia LIMOS, CNRS UMR 6158, University Blaise Pascal, Clermont-Ferrand, France INRIA NGE/LORIA, Nancy, France Universidad de Málaga, Spain Palacký University Olomouc, Czech Republic
Alexandre Bazin Sadok Ben Yahia Radim Belohlavek Karell Bertet François Brucker Ana Burusco Aleksey Buzmakov
Pablo Cordero M. Eugenia Cornejo Jean Diatta Stephan Doerfel Xavier Dolques
Robert Godin Dmitry Ignatov
Stanislav Krajci Ondrej Kridlo Francesco Kriegel Jan Konecny Michal Krupka Marzena Kryszkiewicz Leonard Kwuida Florence Le Ber
Shefield Hallam University, United Kingdom Pavol Jozef Šafárik University in Košice, Slovakia Facultad de Ingenieria - Universidad Austral, Argentina Ciències de la Computació, Catalonia, Spain Université de Lorraine, France Tallinn University of Technology, Estonia Palacký University, Olomouc, Czech Republic Laboratory L3I, University of La Rochelle, France Ecole Centrale Marseille, France Universidad Publica De Navarra, Spain INRIA-LORIA (CNRS-Université de Lorraine), Nancy, France IRISA/INSA Rennes, France Universidad de Málaga, Spain University of Cadiz, Spain Université de la Réunion, France University of Kassel, Germany ICube, Université de Strasbourg/ENGEES, Strasbourg, France Akita International University, Japan University of Malaga, Spain Universite de Rennes 1, France LITA, Université Lorraine, Metz, France Université du Québec à Montréal, Canada National Research University Higher School of Economics, Russia LIRIS - INSA de Lyon, France P. J. Safarik University, Slovak Republic Pavol Jozef Šafárik University in Košice, Slovakia Technische Universität Dresden, Germany Palacký University, Olomouc, Czech Republic Palacký University, Olomouc, Czech Republic Warsaw University of Technology, Poland Bern University of Applied Sciences, Switzerland ICube, Université de Strasbourg/ENGEES, Strasbourg, France Université du Québec en Outaouais, LARIM, Canada University of Cádiz, Spain
LIMOS – Blaise Pascal University – CNRS, France Angel Mora University of Malaga, Spain Amedeo Napoli LORIA Nancy, France Sergei Obiedkov National Research University Higher School of Economics, Russia Manuel Ojeda-Aciego Dept. of Applied Mathematics, University of Malaga,
Spain Jan Outrata Palacký University, Olomouc, Czech Republic Jean-Marc Petit Université de Lyon, INSA Lyon, France Uta Priss Ostfalia University, Germany Olivier Raynaud LIMOS – Blaise Pascal University – CNRS, France Sándor Radeleczki Department of Analysis, University of Miskolc, Hungary François Rioult GREYC CNRS UMR6072 – Université de Caen, France Sebastian Rudolph Technische Universität Dresden, Germany Christian Sacarea Babes-Bolyai University, Romania Barış Sertkaya Frankfurt University of Applied Sciences, Germany Gerd Stumme University of Kassel, Germany Laszlo Szathmary University of Debrecen, Hungary Andreja Tepavcevic University of Novi Sad, Serbia Petko Valtchev Université du Québec À Montréal, Canada Francisco J. Valverde-Albacete Universidad Rey Juan Carlos, Madrid, Spain Bruce Watson Stellenbosch University, South Africa Yiyu Yao University of Regina, Canada
Bernard De Baets Department of Data Analysis and Mathematical Modelling.
Ghent University, Belgium.
Abstract: Decision making inevitably involves the comparison and ordering of real variables. In the presence of uncertainty, this entails the comparison of real-valued random variables. We briefly review three approaches to such comparison: 1. Stochastic dominance: an approach based on a pointwise comparison of cumulative distribution functions; 2. Statistical preference: an approach based on a pairwise comparison in terms of winning probabilities; 3. Probabilistic preference: an approach based on multivariate winning probabilities. Whereas the first and third approaches are intrinsically transitive, the second approach requires considerable mathematical efort to unveil the underlying transitivity properties. Moreover, the first approach ignores the possible dependence between the random variables and is based on univariate distribution functions, the second approach is by definition built on bivariate joint distribution functions, while the third approach is based on the overall joint distribution function.
Tarmo Uustalu
Dept. of Computer Science of Reykjavik University (Iceland) Abstract: This talk is not on formal concept analysis, but on a diferent new application of Chu sṕaces to computer science, specifically to programming language semantics.
It is standard in this domain to use monads to model notions of computation that involve efects such as computation with input/output, manipulation of store, nondeterminism. An efectful computation cannot return a value on its own: it issues requests to the outside world and needs these responded to make progress. To run, it needs ot be paired with an environment that can service these requests. Such notions of environment are modelled with comonads. Protocols of communication between computations and environments admit mathematization by what we call monad-comonad interaction laws. Those are monoid objects in some category of Chu spaces.
I will introduce and explain some basics about monad-comonad interaction laws.
John F. Sowa
Kyndi, Inc., San Mateo, CA, USA Abstract: In 2000, Tim Berners-Lee proposed a vision for the Semantic Web that was more ambitious than the tools delivered in 2005. Since then. better technology has been developed for artificial intelligence, natural language processing, and automated reasoning. But the complexity of the new tools is beyond the expertise of most programmers. Fortunately, the technology can also support automated and semi-automated methods that can simplify the interface for both programmers and end users. This talk surveys technology that can enable anybody at any level of expertise to use, control, and interact with computer systems.
Peter Vojtáš
Charles University, Prague, Czech Republic Abstract: We are motivated by the development from Galois-Tukey connections in set-theory via Question-Answer category in complexity to Challenge-Response reductions of real situations to models. We describe several experiments (use-case, data, prototype, metrics, evaluation, comparison) in computer science ranging from recommender systems to web semantization. We see this as learning of concepts of e.g., user preferred objects or automated tagging.
Boualem Benatallah
Dublin City University, Ireland Abstract: AI enabled augmentation promises to transform services through data-driven automation and insights. The entire service economy is rapidly shifting to AI enabled augmentation, embracing deep changes that are required for increased productivity and efectiveness. Nonetheless, despite the early adoption, AI augmented service technologies are still only in their preliminary stages of development, with several unsolved challenges stemming from lack of computational abstractions and models to reason about ambiguity and uncertainty that are inherent in data-driven processes. We will revisit abstractions, concepts, and techniques in data-driven service models and middleware. A key challenge also lies the synergy between human and machine, crowd and AI – augmentation will seek to achieve bridging the gap between disparate systems and processes, and between human and machine We will discuss synergies between intent-based composition, composition synthesis, robotic process automation and other technologies as step forward to scale AI augmented services enablement. We will discuss quality control in training data and AI augmented services.