=Paper=
{{Paper
|id=Vol-2972/preface
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2972/preface.pdf
|volume=Vol-2972
}}
==None==
https://ceur-ws.org/Vol-2972/preface.pdf
Workshop Notes
9th International Workshop
“What can FCA do for Artificial Intelligence?”
FCA4AI 2021
30th International Joint Conference on Artificial Intelligence
IJCAI 2021
August 21 2021
Montréal, Québec, Canada
Editors
Sergei O. Kuznetsov (NRU HSE Moscow)
Amedeo Napoli (LORIA Nancy)
Sebastian Rudolph (TU Dresden)
http://fca4ai.hse.ru/2021/
Preface
The eight editions of the FCA4AI Workshop showed that many researchers working in Artificial
Intelligence are deeply interested by a well-founded method for classification and data mining such
as Formal Concept Analysis (see https://conceptanalysis.wordpress.com/fca/).
FCA4AI started with ECAI 2012 (Montpellier) and the last edition was co-located with ECAI
2020 (Santiago de Compostela, virtual conference). The FCA4AI workshop has now a quite long his-
tory and all the proceedings are available as CEUR proceedings (see http://ceur-ws.org/, volumes
939, 1058, 1257, 1430, 1703, 2149, 2529, and 2729). This year, the workshop has again attracted
researchers from many different countries working on actual and important topics related to FCA,
showing the diversity and the richness of the relations between FCA and AI.
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis
and classification. FCA allows one to build a concept lattice and a system of dependencies (implica-
tions and association rules) which can be used for many AI needs, e.g. knowledge discovery, machine
learning, knowledge representation, reasoning, ontology engineering, as well as information retrieval
and text processing. Recent years have been witnessing increased scientific activity around FCA, in
particular a strand of work emerged that is aimed at extending the possibilities of FCA w.r.t. knowl-
edge processing. These extensions are aimed at allowing FCA to deal with more complex data, both
from the data analysis and knowledge discovery points of view. Actually these investigations provide
new possibilities for AI practitioners within the framework of FCA. Accordingly, we are interested in
the following issues:
• How can FCA support AI activities such as knowledge processing, i.e. knowledge discovery,
knowledge representation and reasoning, machine learning (clustering, pattern and data min-
ing), natural language processing, information retrieval. . .
• How can FCA be extended in order to help AI researchers to solve new and complex problems
in their domains, in particular how to combine FCA with neural classifiers for improving
interpretability of the output and producing valuable explanations. . .
The workshop is dedicated to discussion of such issues. First of all we would like to thank all the
authors for their contributions and all the PC members for their reviews and precious collaboration.
This year, 24 papers were submitted and 14 were accepted for presentation at the workshop, out of
which 6 short papers. The papers submitted to the workshop were carefully peer-reviewed by three
members of the program committee. Finally, the order of the papers in the proceedings (see page 5)
follows the program order (see http://fca4ai.hse.ru/2021/).
The Workshop Chairs
Sergei O. Kuznetsov
National Research University Higher School of Economics, Moscow, Russia
Amedeo Napoli
Université de Lorraine, CNRS, Inria, LORIA, 54000 Nancy, France
Sebastian Rudolph
Technische Universität Dresden, Germany
Copyright c 2021 for this paper by its authors. Use permitted under Creative Commons License Attri-
bution 4.0 International (CC BY 4.0).
Program Committee
Mehwish Alam (AIFB Institute, FIZ KIT Karlsruhe, Germany)
Jaume Baixeries (UPC Barcelona, Catalunya)
Karell Bertet (L3I, Université de La Rochelle, France)
Aleksey Buzmakov (National Research University HSE Perm, Russia)
MiguelCouceiro (LORIA, Nancy France)
Diana Cristea (Babes-Bolyai University, Cluj-Napoca, Romania)
Mathieu D’Aquin (National University of Ireland Galway, Ireland)
Florent Domenach (Akita International University, Japan)
Elizaveta Goncharova (NRU Higher School of Economics, Moscow, Russia)
Tom Hanika (University of Kassel, Germany)
Marianne Huchard (LIRMM/Université de Montpellier, France)
Dmitry I. Ignatov (National Research University HSE Moscow, Russia)
Dmitry Ilvovsky (NRU Higher School of Economics, Moscow, Russia)
Mehdi Kaytoue (Infologic, Lyon, France)
Jan Konecny (Palacky University, Olomouc, Czech Republic)
Francesco Kriegel (Technische Universität Dresden, Germany)
Leonard Kwuida (Bern University of Applied Sciences, Switzerland)
Florence Le Ber (ENGEES/Université de Strasbourg, France)
Tatiana Makhalova (National Research University HSE Moscow, Russia, and Inria LORIA,
Nancy, France)
Nizar Messai (Université François Rabelais Tours, France)
Rokia Missaoui (UQO Ottawa, Canada)
Sergei A. Obiedkov (NRU Higher School of Economics, Moscow, Russia)
Uta Priss (Ostfalia University, Wolfenbüttel, Germany)
Christian Sacarea (Babes-Bolyai University, Cluj-Napoca, Romania)
Henry Soldano (Laboratoire d’Informatique de Paris Nord, Paris, France)
Francisco José Valverde Albacete (Universidad Carlos III de Madrid, Spain)
Renato Vimieiro (Universidade Federal de Minas Gerais, Belo Horizonte, Brazil)
Contents
1 Modelling Conceptual Schemata with Formal Concept Analysis
Uta Priss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Data Overview by Means of Delta-Classes of Equivalence. The Case of the Titanic
Dataset
Aleksey Buzmakov, Sergei O. Kuznetsov, Tatiana Makhalova, and Amedeo Napoli . . 19
3 FCA Went (Multi-)Relational, But Does It Make Any Difference?
Mickaël Wajnberg, Petko Valtchev, Mario Lezoche, Alexandre Blondin-Massé, and
Hervé Panetto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4 Likely-Occurring Itemsets for Pattern Mining
Tatiana Makhalova, Sergei O. Kuznetsov, and Amedeo Napoli . . . . . . . . . . . . . . 39
5 Concept-based Chatbot for Interactive Query Refinement in Product Search
Elizaveta Goncharova, Dmitry Ilvovsky, and Boris Galitsky . . . . . . . . . . . . . . . 51
6 Variability Extraction from Simulator I/O Data Schemata in Agriculture Decision-
Support Software
Thomas Georges, Marianne Huchard, Mélanie König, Clémentine Nebut, and Chouki
Tibermacine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
7 Multimodal Clustering with Evolutionary Algorithms
Mikhail Bogatyrev, Dmitry Orlov, and Tatiana Shestaka . . . . . . . . . . . . . . . . . 71
8 On Suboptimality of GreConD for Boolean Matrix Factorisation of Contranominal
Scales
Dmitry Ignatov and Alexandra Yakovleva . . . . . . . . . . . . . . . . . . . . . . . . . 87
9 Summation of Decision Trees
Egor Dudyrev and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . . . . . . . . . . 99
10 Ensemble Techniques for Lazy Classification Based on Pattern Structures
Ilya Semenkov and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . . . . . . . . . . 105
11 A Concept of Self-Supervised Logical Rule Inference in Symbolic Classifications
Xenia Naidenova and Vladimir Parkhomenko . . . . . . . . . . . . . . . . . . . . . . . 113
12 Non-Redundant Link Keys in RDF Data: Preliminary Steps
Nacira Abbas, Alexandre Bazin, Jérôme David, and Amedeo Napoli . . . . . . . . . . 125
13 Formal Concept Analysis for Semantic Compression of Knowledge Graph Versions
Damien Graux, Diego Collarana, and Fabrizio Orlandi . . . . . . . . . . . . . . . . . . 131