10th International Workshop What can FCA do for Articial Intelligence?

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FCA4AI 2022

The nine preceding 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/).

The FCA4AI Workshop Series started with ECAI 2012 (Montpellier) and the last edition was

co-located with IJCAI 2021 (Montréal, Canada). 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, 2729, and 2972). 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. This year it can be noticed that researchers

are mostly interested in XAI and using FCA for providing explanations in Knowledge Discovery, and also in NLP, which is nowadays a very important line of investigation.

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. 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/2022/).

The Workshop Chairs

Sergei O. Kuznetsov

National Research University Higher School of Economics, Moscow, Russia

Sebastian Rudolph

Technische Universität Dresden, Germany

Copyright '2022 for the individual papers by the papers’ authors. Copyright '2022 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

Program

Committee

1 2 3 4 5 6 7 8

Intrinsically Interpretable Document Classication via Concept Lattices Eric George Parakal and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . .

Towards Fast Finding Optimal Short Classiers Egor Dudyrev and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . . . . .

Can FCA Provide a Framework for Articial General Intelligence?

Francisco J. Valverde-Albacete, Carmen PelÆez-Moreno, Inma P. Cabrera, Pablo Cordero, and Manuel Ojeda-Aciego . . . . . . . . . . . . . . . . . . . .

Small Overtting Probability in Minimization of Empirical Risk for FCA-based

Machine Learning Dmitry V. Vinogradov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Framework for Pareto-Optimal Multimodal Clustering Mikhail Bogatyrev and Dmitry Orlov . . . . . . . . . . . . . . . . . . . . . . .

Lazy Classication of Underground Forums Messages Using Pattern Structures Abdulrahim Ghazal and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . .

Organizing Contexts as a Lattice of Decision Trees for Machine Reading Comprehension Boris Galitsky, Dmitry Ilvovsky, and Elizaveta Goncharova . . . . . . . . . . . 7

9 23 35 41 51 63

References