Workshop Notes




                     International Workshop
          “What can FCA do for Artificial Intelligence?”
                            FCA4AI


   International Joint Conference on Artificial Intelligence
                           IJCAI 2013
                          August 4, 2013

                           Beijing, China



Editors
Sergei O. Kuznetsov (NRU HSE Moscow)
Amedeo Napoli (LORIA Nancy)
Sebastian Rudolph (TU Dresden)



                     http://fca4ai.hse.ru/2013/
2
                  What FCA Can Do for Artificial Intelligence?
                       FCA4AI: An International Workshop


                                           Preface

    This is the second edition of the FCA4AI workshop, the first edition being associated to
the ECAI 2012 Conference, held in Montpellier, in August 2012 (see http://www.fca4ai.
hse.ru/). In particular, the first edition of the workshop showed that there are many AI
researchers interested in FCA. Based on that, the three co-editors decided to organize a
second edition of the FCA4AI workshop at the IJCAI 2013 Conference in Beijing.
   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 processing
involving learning, knowledge discovery, knowledge representation and reasoning, ontology
engineering, as well as information retrieval and text processing. Thus, there exist 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 from the knowledge
representation point of view, including, e.g., ontology engineering.
    All these works extend the capabilities of FCA and offer 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, 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. The papers submitted to the workshop
were carefully peer-reviewed by two 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. We also thank the organizing committee of ECAI-2012 and
especially workshop chairs Jérôme Lang and Michèle Sebag for the support of the workshop.


     The Workshop Chairs

     Sergei O. Kuznetsov
     National Research University Higher Schools of Economics, Moscow, Russia

     Amedeo Napoli
     LORIA (CNRS – INRIA – Université de Lorraine), Vandoeuvre les Nancy, France

     Sebastian Rudolph
     Technische Universität Dresden, Germany


                                               3
Program Committee
 Mathieu D’Aquin (Open University, UK)

 Franz Baader (Technische Universität Dresden, Germany)

 Karell Bertet (Université de La Rochelle, France, Germany)

 Claudio Carpineto (Fondazione Ugo Bordoni, Roma, Italy)

 Felix Distel (Technische Universität Dresden, Germany)

 Peter Eklund (University of Wollongong, Australia)

 Sébastien Ferré (IRISA Rennes, France)

 Pascal Hitzler (Wright State University, Dayton, Ohio, USA)

 Dmitry I. Ignatov (NRU Higher School of Economics, Moscow, Russia)

 Mehdi Kaytoue (INSA - LIRIS Lyon, France)

 Markus Krötzsch (University of Oxford, UK)

 Sergei A. Obiedkov (NRU Higher School of Economics, Moscow, Russia)

 Uta Priss (Ostfalia University of Applied Sciences, Wolfenbüttel, Germany)

 Baris Sertkaya (SAP Dresden, Germany)

 Henry Soldano (Université de Paris-Nord, France)




                                        4
Table of Contents


2   FCA and pattern structures for mining care trajectories
    Aleksey Buzmakov, Elias Egho, Nicolas Jay, Sergei O. Kuznetsov, Amedeo
    Napoli and Chedy Raı̈ssi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    7
3   Using pattern structures to support information retrieval with Formal Concept
    Analysis
    Vı́ctor Codocedo, Ioanna Lykourentzou, Hernan Astudillo and Amedeo Napoli 15
4    FCA-Based Concept Detection in a RosettaNet PIP Ontology
    Jamel Eddine Jridi and Guy Lapalme . . . . . . . . . . . . . . . . . . . . . .         25
5    Bases via Minimal Generator
    Pablo Cordero, Manuel Enciso, Angel Mora Bonilla and Manuel Ojeda-Aciego 33
6   Debugging Program Code Using Implicative Dependencies
    Artem Revenko . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    37
7   Practical Computing with Pattern Structures in FCART Environment
    Aleksey Buzmakov and Alexey Neznanov . . . . . . . . . . . . . . . . . . . .           49
8    Towards Knowledge Structuring of Sensor Data Based on FCA and Ontology
    Peng Wang, Wenhuan Lu, Zhaopeng Meng, Jianguo Wei and Françoise Fogelman-
    Soulié . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57




                                           5
6