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https://ceur-ws.org/Vol-1257/preface.pdf
Workshop Notes
International Workshop
“What can FCA do for Artificial Intelligence?”
FCA4AI
European Conference on Artificial Intelligence
ECAI 2014
August 19, 2014
Prague, Czech Republic
Editors
Sergei O. Kuznetsov (NRU HSE Moscow)
Amedeo Napoli (LORIA Nancy)
Sebastian Rudolph (TU Dresden)
http://fca4ai.hse.ru/2014/
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Preface
The first and the second edition of the FCA4AI Workshop showed that many researchers
working in Artificial Intelligence are indeed interested by a well-founded method for classi-
fication and mining such as Formal Concept Analysis (see http://www.fca4ai.hse.ru/).
The first edition of FCA4AI was co-located with ECAI 2012 in Montpellier and published as
http://ceur-ws.org/Vol-939/ while the second edition was co-located with IJCAI 2013
in Beijing and published as http://ceur-ws.org/Vol-1058/. Based on that, we decided
to continue the series and we took the chance to organize a new edition of the workshop in
Prague at the ECAI 2014 Conference. This year, the workshop has again attracted many
different researchers working on actual and important topics, e.g. recommendation, linked
data, classification, biclustering, parallelization, and various applications. This shows the
diversity and the richness of the relations between FCA and AI. Moreover, this is a good
sign for the future and especially for young researchers that are at the moment working in
this area or who will do.
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) which can be used for many AI needs, e.g. knowledge discovery,
learning, knowledge representation, reasoning, ontology engineering, as well as information
retrieval and text processing. As we can see, there are many “natural links” between FCA
and AI.
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, such as work on pattern structures and relational context analysis. These
extensions are aimed at allowing FCA to deal with more complex than just binary data,
both from the data analysis and knowledge discovery points of view and as well from the
knowledge representation point of view, including, e.g., ontology engineering.
All these investigations provide new possibilities for AI activities in the framework of
FCA. Accordingly, in this workshop, we are interested in two main issues:
• 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 AI researchers to solve new and complex
problems in their domains.
The workshop is dedicated to discuss such issues. This year, the papers submitted to the
workshop were carefully peer-reviewed by three members of the program committee and 11
papers with the highest scores were selected. We thank all the PC members for their reviews
and all the authors for their contributions.
The Workshop Chairs
Sergei O. Kuznetsov
National Research University, Higher Schools of Economics, Moscow, Russia
Amedeo Napoli
LORIA (CNRS – Inria Nancy Grand Est – Université de Lorraine), Vandoeuvre les Nancy,
France
Sebastian Rudolph
Technische Universitaet Dresden, Germany
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Program Committee
Mathieu D’Aquin (Open University, UK)
Jaume Baixeries, UPC Barcelona, Catalunya
Karell Bertet (Université de La Rochelle, France, Germany)
Claudio Carpineto (Fondazione Ugo Bordoni, Roma, Italy)
Felix Distel (Technische Universitaet Dresden, Germany)
Florent Domenach (University of Nicosia, Cyprus)
Peter Eklund (University of Wollongong, Australia)
Cynthia-Vera Glodeanu (Technische Universitaet Dresden, Germany)
Marianne Huchard (LIRMM/Université de Montpellier, France)
Dmitry I. Ignatov (NRU Higher School of Economics, Moscow, Russia)
Mehdi Kaytoue (INSA-LIRIS Lyon, France)
Florence Le Ber, Université de Strasbourg, France
Nizar Messai (Université de Tours, France)
Sergei A. Obiedkov (NRU Higher School of Economics, Moscow, Russia)
Jan Outrata (Palacky University, Olomouc, Czech Republic)
Jean-Marc Petit (INSA-LIRIS Lyon, France)
Uta Priss (Ostfalia University of Applied Sciences, Wolfenbüttel, Germany)
Chedy Raı̈ssi (Inria/LORIA Nancy, France)
Artem Revenko, Technische Universitaet Dresden, Germany
Christian Sacarea (Babes-Bolyai University, Cluj-Napoca, Romania)
Baris Sertkaya (SAP Dresden, Germany)
Henry Soldano (Université de Paris-Nord, France)
Laszlo Szathmary, University of Debrecen, Hungary
Petko Valtchev (Université du Québec à Montréal, Montréal, Canada)
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Table of Contents
Contents
1 Invited Talk
Abstraction, taxonomies, connectivity: from AI to FCA and back
Henry Soldano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Using Formal Concept Analysis to Create Pathways through Museum Collections
Tim Wray and Peter Eklund . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Can FCA-based Recommender System Suggest a Proper Classifier?
Yury Kashnitsky and Dmitry Ignatov . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4 Bicluster enumeration using Formal Concept Analysis
Victor Codocedo and Amedeo Napoli . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5 Towards an FCA-based Recommender System for Black-Box Optimization
Josefine Asmus, Daniel Borchmann, Ivo F. Sbalzarini, and Dirk Walther . . . . . . . . 35
6 Generalization and Modification of Classification Algorithms Based on Formal Con-
cept Analysis
Evgeny Kolmakov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
7 Concept Stability as a Tool for Pattern Selection
Aleksey Buzmakov, Sergei O. Kuznetsov, and Amedeo Napoli . . . . . . . . . . . . . . 51
8 About Universality and Flexibility of FCA-based Software Tools
A.A. Neznanov and A.A. Parinov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
9 PRCA – A Parallel Relational Concept Analysis Framework
Ines Moosdorf, Adrian Paschke, Alexandru Todor, Jens Dietrich, and Hans W. Guesgen 67
10 Concept Building with Non-Hierarchical Relations Extracted from Text – Comparing
a Purely Syntactical Approach to a Semantic one
Silvia Moraes, Vera Lima, and Luis Furquim . . . . . . . . . . . . . . . . . . . . . . . 77
11 What can FCA do for database linkkey extraction?
Manuel Atencia, Jérôme David, and Jérôme Euzenat . . . . . . . . . . . . . . . . . . . 85
12 Lattice-Based View Access: A way to Create Views over SPARQL Query for Knowl-
edge Discovery
Mehwish Alam and Amedeo Napoli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
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