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https://ceur-ws.org/Vol-2529/preface.pdf
Workshop Notes
Seventh International Workshop
“What can FCA do for Artificial Intelligence?”
FCA4AI 2019
International Joint Conference on Artificial Intelligence
IJCAI 2019
August 10 2019
Macao, China
Editors
Sergei O. Kuznetsov (NRU HSE Moscow)
Amedeo Napoli (LORIA Nancy)
Sebastian Rudolph (TU Dresden)
http://fca4ai.hse.ru/2019/
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Preface
The six preceding editions of the FCA4AI Workshop showed that many researchers work-
ing in Artificial Intelligence are deeply interested by a well-founded method for classification
and mining such as Formal Concept Analysis (see http://www.fca4ai.hse.ru/). FCA4AI
was co-located with ECAI 2012 (Montpellier), IJCAI 2013 (Beijing), ECAI 2014 (Prague),
IJCAI 2015 (Buenos Aires), ECAI 2016 (The Hague), and finally with IJCAI/ECAI 2018
(Stockholm). All the proceedings of the preceding editions are published as CEUR Proceed-
ings (http://ceur-ws.org/Vol-939/, http://ceur-ws.org/Vol-1058/, http://ceur-ws.
org/Vol-1257/, and http://ceur-ws.org/Vol-1430/, http://ceur-ws.org/Vol-1703/,
and http://ceur-ws.org/Vol-2149/). This year, the workshop has again attracted re-
searchers 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 depen-
dencies (implications) which can be used for many Artificial Intelligence needs, e.g. knowledge
discovery, learning, knowledge representation, reasoning, ontology engineering, as well as in-
formation 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, such as work on pattern structures and
relational context analysis. These extensions are aimed at allowing FCA to deal with more
complex data, both from the data analysis and knowledge discovery points of view. Then
these investigations provide new possibilities for AI practitioners in the framework of FCA.
Accordingly, we are interested and discuss the following issues at FCA4AI:
• How can FCA support AI activities such as knowledge processing (knowledge discov-
ery, knowledge representation and reasoning), learning (clustering, pattern and data
mining), natural language processing, and information retrieval.
• How can FCA be extended in order to help Artificial Intelligence researchers to solve
new and complex problems in their domains.
In addition, the 3rd workshop on “Formal Concept Analysis for Knowledge Discovery”
(FCA4KD 2019) was held at the Faculty of Computer Science of National Research University
Higher School of Economics (NRU HSE, Moscow, Russia) on June 7, 2019. FCA4KD is an
event which is close to FCA4AI, as the goal of the FCA4KD is to attract researchers applying
FCA-based methods of knowledge discovery in various subject domains. There was an invited
talk by Andrey Rodin on the problem of justification of knowledge discovery. In addition,
there were 6 regular contributions, three of which were selected for the current volume.
Sergei O. Kuznetsov would like to acknowledge the support of the NRU HSE University
Basic Research Program funded by the Russian Academic Excellence Project 5-100.
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
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Program Committee
Jaume Baixeries (UPC Barcelona, Catalunya),
Aleksey Buzmakov (National Research University HSE Perm, Russia),
Victor Codocedo (UFTSM Santiago de Chile, Chile),
Elizaveta Goncharova (NRU Higher School of Economics, Moscow, Russia),
Marianne Huchard (LIRMM/Université de Montpellier, France),
Dmitry I. Ignatov (National Research University HSE Moscow, Moscow, Russia),
Sergei Kuznetsov (National Research University HSE Moscow, Russia),
Mehdi Kaytoue (INSA-LIRIS Lyon, France),
Florence Le Ber (ENGEES/Université de Strasbourg, France),
Amedeo Napoli (Université de Lorraine, CNRS, Inria, LORIA, 54000 Nancy, France),
Sergei A. Obiedkov (NRU Higher School of Economics, Moscow, Russia),
Sebastian Rudolph (Technische Universität Dresden, Germany),
Dmitry Vinogradov (Russian Academy of Science, Moscow, Russia).
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Contents
1 Enabling natural language analytics over relational data using Formal Concept Anal-
ysis (short paper)
C. Anantaram, Mouli Rastogi, Mrinal Rawat, and Pratik Saini . . . . . . . . . . . . . 7
2 Using Formal Concept Analysis to Explain Black Box Deep Learning Classification
Models (short paper)
Amit Sangroya, C. Anantaram, Mrinal Rawat, and Mouli Rastogi . . . . . . . . . . . . 19
3 Validating Correctness of Textual Explanation with Complete Discourse trees
Boris Galitsky and Dmitry Ilvovsky . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4 Least General Generalization of the Linguistic Structures (short paper)
Boris Galitsky and Dmitry Ilvovsky . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Truth and Justification in Knowledge Representation
Andrei Rodin and Serge Kovalyov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
6 FCA-based Approach to Machine Learning (short paper)
Dmitry V. Vinogradov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
7 Clustering of Biomedical Data Using the Greedy Clustering Algorithm Based on In-
terval Pattern Concepts (short paper)
Alexey V. Galatenko, Stepan A. Nersisyan, and Vera V. Pankratieva . . . . . . . . . . 65
8 Increasing the efficiency of packet classifiers with closed descriptions
Elizaveta Goncharova and Sergei Kuznetsov . . . . . . . . . . . . . . . . . . . . . . . . 75
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