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Regular version of the site
Workshop of ECAI 2014 conference, Prague, Czech Republic

3rd Workshop 'What can FCA do for Artificial Intelligence?'


The first and second editions of the FCA4AI Workshop (ECAI 2012, Montpellier and IJCAI 2013, Beijing) showed that many researchers working in Artificial Intelligence are indeed interested by a powerful method for classification and mining such as Formal Concept Analysis (see the proceedings of the first and second editions of the workshop).
We have the chance to organize a new edition of the workshop in Prague at the ECAI 2014 Conference.

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, and 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. 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 point 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 will be 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 domain.

The workshop is dedicated to discuss such issues.

Topics of interest include but are not limited to:

  • Concept lattices and related structures: description logics, pattern structures, relational structures.
  • Knowledge discovery and data mining with FCA: association rules, itemsets and data dependencies, attribute implications, data pre-processing, redundancy and dimensionality reduction, classification and clustering.
  • Knowledge engineering and ontology engineering: knowledge representation and reasoning.
  • Scalable algorithms for concept lattices and artificial intelligence "in the large" (distributed aspects, big data).
  • Applications of concept lattices: semantic web, information retrieval, visualization and navigation, pattern recognition.

The workshop will include time for audience discussion for having a better understanding of the issues, challenges, and ideas being presented.


The workshop welcomes submissions in pdf format in Springer's LNCS style. Submissions can be

  • technical papers not exceeding 8 pages,
  • system descriptions or position papers on work in progress not exceeding 4 pages

Submissions are via EasyChair
The workshop proceedings will be published as CEUR proceedings. 
A selection of the best papers presented at the workshop will be considered for a special issue of a high-level journal.


  • Sergei Kuznetsov, Higher Schools of Economics, Moscow, Russia
  • Amedeo Napoli, LORIA-INRIA, Vandoeuvre les Nancy, France
  • Sebastian Rudolph, Technische Universitaet Dresden, Germany