FCA4AI http://www.fca4ai.hse.ru
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 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 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 from the knowledge representation point of view, including, e.g., ontology engineering.
All these works extend the capabilities of FCA and o er 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, 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 Jer^ome Lang and Michele Sebag for the support of the workshop.
Sergei O. Kuznetsov National Research University Higher Schools of Economics, Moscow, Russia Amedeo Napoli LORIA (CNRS { INRIA { Universite de Lorraine), Vandoeuvre les Nancy, France Sebastian Rudolph AIFB Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany Program Committee
Franz Baader (Technische Universitat Dresden, Germany) Radim Belohlavek (Palacky University, Olomouc, Czech Republic) Claudio Carpineto (Fondazione Ugo Bordoni, Roma, Italy) Felix Distel (Technische Universitat Dresden, Germany) Sebastien Ferre (IRISA Rennes, France) Bernhard Ganter (Technische Universitat Dresden, Germany) Pascal Hitzler (Wright State University, Dayton, Ohio, USA) Marianne Huchard (LIRMM Montpellier, France) Dmitry I. Ignatov (NRU Higher School of Economics, Moscow, Russia) Mehdi Kaytoue (Universidade Federal Minas Gerais, Belo Horizonte, Brazil) Markus Krotzsch (University of Oxford, UK) Sergei A. Obiedkov (NRU Higher School of Economics, Moscow, Russia) Uta Priss (Ostfalia University of Applied Sciences, Wolfenbuttel, Germany) Baris Sertkaya (SAP Dresden, Germany) Gerd Stumme (Universitat Kassel, Germany) Petko Valtchev (Universite du Quebec a Montreal, Montreal, Canada) Formal Concept Analysis Applied to Transcriptomic Data Mehwish Alam, Adrien Coulet, Amedeo Napoli and Malika Smal-Tabbone . A New Approach to Classi cation by Means of Jumping Emerging Patterns Aleksey Buzmakov, Sergei O. Kuznetsov, and Amedeo Napoli . . . . . . . . . Semantic Querying of Data Guided by Formal Concept Analysis V ctor Codocedo, Ioanna Lykourentzou and Amedeo Napoli . . . . . . . . . . Information Retrieval by On-line Navigation in the Latticial Space-search of a Database, with Limited Objects Access Christophe Demko and Karell Bertet . . . . . . . . . . . . . . . . . . . . . . . Relational Data Exploration by Relational Concept Analysis Xavier Dolques, Marianne Huchard, Florence Le Ber and Clementine Nebut . Let the System Learn a Game: How Can FCA Optimize a Cognitive Memory Structure William Dyce, Thibaut Marmin, Namrata Patel, Clement Sipieter, Guillaume Tisserant and Violaine Prince . . . . . . . . . . . . . . . . . . . . . . . . . . . An Approach to Semantic Content Based Image Retrieval Using Logical Concept Analysis. Application to Comicbooks Clement Guerin, Karell Bertet and Arnaud Revel . . . . . . . . . . . . . . . . Classi cation Reasoning as a Model of Human Commonsense Reasoning Xenia A. Naidenova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Finding Errors in New Object in Formal Contexts Artem Revenko, Sergei O. Kuznetsov and Bernhard Ganter . . . . . . . . . . Finding Minimal Rare Itemsets in a Depth- rst Manner Laszlo Szathmary, Petko Valtchev, Amedeo Napoli and Robert Godin . . . . A System for Knowledge Discovery in Big Dynamical Text Collections Sergei O. Kuznetsov, Alexey A. Neznanov and Jonas Poelmans . . . . . . . . 5 7 15 23 33 41 45 53 57 65 73 81