\What can FCA do for Arti cial Intelligence?" FCA4AI European Conference on Arti cial Intelligence
ECAI 2014
Preface
The rst and the second edition of the FCA4AI Workshop showed that many researchers working in Arti cial Intelligence are indeed interested by a well-founded method for classication and mining such as Formal Concept Analysis (see http://www.fca4ai.hse.ru/). The rst 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 di erent researchers working on actual and important topics, e.g. recommendation, linked data, classi cation, 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 classi cation. 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 scienti c 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 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 discovery, 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.
Sergei O. Kuznetsov National Research University, Higher Schools of Economics, Moscow, Russia Amedeo Napoli LORIA (CNRS { Inria Nancy Grand Est { Universite de Lorraine), Vandoeuvre les Nancy, France Sebastian Rudolph Technische Universitaet Dresden, Germany
Jaume Baixeries, UPC Barcelona, Catalunya Karell Bertet (Universite 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/Universite de Montpellier, France) Dmitry I. Ignatov (NRU Higher School of Economics, Moscow, Russia) Mehdi Kaytoue (INSA-LIRIS Lyon, France) Florence Le Ber, Universite de Strasbourg, France Nizar Messai (Universite 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, Wolfenbuttel, Germany) Chedy Rassi (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 (Universite de Paris-Nord, France) Laszlo Szathmary, University of Debrecen, Hungary Petko Valtchev (Universite du Quebec a Montreal, Montreal, Canada)
Abstraction, taxonomies, connectivity: from AI to FCA and back
Henry Soldano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 3 4 5 6 7 8
Using Formal Concept Analysis to Create Pathways through Museum Collections Tim Wray and Peter Eklund . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Can FCA-based Recommender System Suggest a Proper Classi er? Yury Kashnitsky and Dmitry Ignatov . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Victor Codocedo and Amedeo Napoli . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Towards an FCA-based Recommender System for Black-Box Optimization Jose ne Asmus, Daniel Borchmann, Ivo F. Sbalzarini, and Dirk Walther . . . . . . . . 35
Generalization and Modi cation of Classi cation Algorithms Based on Formal Concept Analysis Evgeny Kolmakov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Aleksey Buzmakov, Sergei O. Kuznetsov, and Amedeo Napoli . . . . . . . . . . . . . . 51 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 12 Lattice-Based View Access: A way to Create Views over SPARQL Query for Knowledge Discovery Mehwish Alam and Amedeo Napoli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93