9th International Workshop “What can FCA do for Artificial Intelligence?”

Abstract

30th International Joint Conference on Artificial Intelligence

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FCA4AI 2021 IJCAI 2021

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 history 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 (implications 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. knowledge 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 mining), 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

Sebastian Rudolph

Technische Universität Dresden, Germany

Copyright c 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Program Committee 1 2 3 4 5 6 7 8 9 10 11 12 13

Modelling Conceptual Schemata with Formal Concept Analysis

Uta Priss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

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

FCA Went (Multi-)Relational, But Does It Make Any Difference?

Mickaël Wajnberg, Petko Valtchev, Mario Lezoche, Alexandre Blondin-Massé, and Hervé Panetto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Likely-Occurring Itemsets for Pattern Mining

Tatiana Makhalova, Sergei O. Kuznetsov, and Amedeo Napoli . . . . . . . . . . . . . . 39

Concept-based Chatbot for Interactive Query Refinement in Product Search

Elizaveta Goncharova, Dmitry Ilvovsky, and Boris Galitsky . . . . . . . . . . . . . . . 51

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

Multimodal Clustering with Evolutionary Algorithms

Mikhail Bogatyrev, Dmitry Orlov, and Tatiana Shestaka . . . . . . . . . . . . . . . . . 71

On Suboptimality of GreConD for Boolean Matrix Factorisation of Contranominal

Scales

Dmitry Ignatov and Alexandra Yakovleva . . . . . . . . . . . . . . . . . . . . . . . . . 87

Summation of Decision Trees

Egor Dudyrev and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Ensemble Techniques for Lazy Classification Based on Pattern Structures

Ilya Semenkov and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . . . . . . . . . . 105

A Concept of Self-Supervised Logical Rule Inference in Symbolic Classifications

Xenia Naidenova and Vladimir Parkhomenko . . . . . . . . . . . . . . . . . . . . . . . 113

Non-Redundant Link Keys in RDF Data: Preliminary Steps

Nacira Abbas, Alexandre Bazin, Jérôme David, and Amedeo Napoli . . . . . . . . . . 125

Formal Concept Analysis for Semantic Compression of Knowledge Graph Versions

Damien Graux, Diego Collarana, and Fabrizio Orlandi . . . . . . . . . . . . . . . . . . 131

References