=Paper=
{{Paper
|id=Vol-1703/preface-2016
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1703/preface-2016.pdf
|volume=Vol-1703
}}
==None==
https://ceur-ws.org/Vol-1703/preface-2016.pdf
Workshop Notes
Fifth International Workshop
“What can FCA do for Artificial Intelligence?”
FCA4AI 2016
European Conference on Artificial Intelligence
ECAI 2016
August 30 2016
The Hague, Netherlands
Editors
Sergei O. Kuznetsov (NRU HSE Moscow)
Amedeo Napoli (LORIA Nancy)
Sebastian Rudolph (TU Dresden)
http://fca4ai.hse.ru/2016/
2
Preface
The four preceding editions of the FCA4AI Workshop showed that many researchers
working in Artificial Intelligence are deeply 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, the second one
with IJCAI 2013 in Beijing, the third one with ECAI 2014 in Prague, and finally the forth
and last one with IJCAI 2015 in Buenos Aires. In addition, all the proceedings of these pre-
ceding editions have been published as CEUR Proceedings (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/).
This year, the fifth workshop has again attracted many different researchers working on
actual and important topics, e.g. theory, fuzzy FCA, dependencies, classification, mining of
linked data, navigation, visualization, and various applications. This shows 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 de-
pendencies (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 in-
vestigations 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 14
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 School of Economics, Moscow, Russia
Amedeo Napoli
LORIA (CNRS – Inria Nancy Grand Est – Université de Lorraine), Vandoeuvre les Nancy,
France
Sebastian Rudolph
Technische Universität Dresden, Germany
3
Program Committee
Mehwish Alam (Université de Paris-Nord, France)
Gabriela Arevalo (Universidad Nacional de Quilmes, Argentina)
Jaume Baixeries (UPC Barcelona, Catalunya)
Karell Bertet (Université de La Rochelle, France, Germany)
Aleksey Buzmakov (National Research University HSE Perm, Russia)
Mathieu D’Aquin (Open University, UK)
Florent Domenach (University of Nicosia, Cyprus)
Sébastien Ferré (IRISA, Rennes, France)
Marianne Huchard (LIRMM/Université de Montpellier, France)
Dmitry I. Ignatov (National Research University HSE Moscow, Moscow, Russia)
Yuri Kashnitsky (National Research University HSE Moscow, Russia)
Mehdi Kaytoue (INSA-LIRIS Lyon, France)
Jan Konecny (Palacky University, Olomouc, Czech Republic)
Florence Le Ber (ENGEES/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)
Christian Săcărea (Babes-Bolyai University, Cluj-Napoca, Romania)
Baris Sertkaya (Frankfurt University of Applied Sciences, Germany)
Diana Troancă (Babes-Bolyai University, Cluj-Napoca, Romania)
Renato Vimiero (UFPE Recife, Brazil)
4
Contents
1 Constraint Programming for Constrained Clustering (Invited Talk)
Christel Vrain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Axiomatization of General Concept Inclusions from Streams of Interpretations with
optional Error Tolerance
Francesco Kriegel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Towards a sequent calculus for formal contexts
Ondrej Kridlo and Manuel Ojeda-Aciego . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4 Morphisms Between Pattern Structures and Their Impact on Concept Lattices
Lars Lumpe and Stefan E. Schmidt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5 A Reachability-based Navigation Paradigm for Triadic Concepts
Diana Troancă . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6 FCA Tools Bundle — a Tool that Enables Dyadic and Triadic Conceptual Navigation
Levente Lorand Kis, Christian Săcareă, and Diana Troancă . . . . . . . . . . . . . . . 43
7 Steps Towards Interactive Formal Concept Analysis with LatViz
Mehwish Alam, Thi Nhu Nguyen Le, and Amedeo Napoli . . . . . . . . . . . . . . . . 51
8 Linked Data Querying through FCA-based Schema Indexing
Dominik Brosius and Steffen Staab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
9 Contribution to the Classification of Web of Data based on Formal Concept Analysis
Justine Reynaud, Yannick Toussaint, and Amedeo Napoli . . . . . . . . . . . . . . . . 69
10 From a possibility theory view of formal concept analysis to the possibilistic handling
of incomplete and uncertain contexts
Zina Ait-Yakoub, Yassine Djouadi, Didier Dubois, and Henri Prade . . . . . . . . . . . 79
11 How Fuzzy FCA and Pattern Structures are connected?
Aleksey Buzmakov and Amedeo Napoli . . . . . . . . . . . . . . . . . . . . . . . . . . 87
12 A tool for classification of sequential data
Giacomo Kahn, Yannick Loiseau, and Olivier Raynaud . . . . . . . . . . . . . . . . . . 97
13 Interval Pattern Concept Lattice as a Classifier Ensemble
Yury Kashnitsky and Sergei O. Kuznetsov . . . . . . . . . . . . . . . . . . . . . . . . . 105
14 New taxonomy of classification methods based on Formal Concepts Analysis
Marwa Trabelsi, Nida Meddouri, and Mondher Maddouri . . . . . . . . . . . . . . . . 113
15 Characterization of Order-like Dependencies with Formal Concept Analysis
Victor Codocedo, Jaume Baixeries, Mehdi Kaytoue, and Amedeo Napoli . . . . . . . . 121
16 A Hybrid Approach for Mining Metabolomic Data
Dhouha Grissa, Blandine Comte, Estelle Pujos-Guillot, and Amedeo Napoli . . . . . . 129
5
6