• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
FCA4AI (Tenth Edition)

What can FCA do for Artificial Intelligence?

co-located with IJCAI-ECAI 2022, Vienna, Austria


IJCAI-ECAI 2022 Conference website




#Call for papers #General information #Topics of interest #Submission details 

Call for papers

We are pleased to announce that the 10th FCA4AI Workshop co-located with the IJCAI-ECAI 2022 Conference that will take place in July 2022.

General information

The preceding editions of the FCA4AI Workshop (from ECAI 2012 until IJCAI 2021) showed that many researchers working in Artificial Intelligence are indeed interested by powerful techniques for classification and data mining provided by Formal Concept Analysis. Again, we have the chance to organize the 10th edition of the workshop in Vienna, co-located with the IJCAI-ECAI 2022 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 and association rules) which can be used for many AI needs, e.g. knowledge processing, knowledge discovery, knowledge representation and reasoning, ontology engineering as well as information retrieval, recommendation, social network analysis and text processing. Thus, 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 plain FCA w.r.t. knowledge processing, such as work on pattern structures and relational context analysis,  as well as on hybridization with other formalisms. These extensions are aimed at allowing FCA to deal with more complex than just binary data, for solving complex problems in data analysis, classification, knowledge processing... While the capabilities of FCA are extended, new possibilities are arising in the framework of FCA.

As usual, the FCA4AI workshop is dedicated to the discussion of such issues, and in particular:

  • How can FCA support AI activities in knowledge discovery, knowledge representation and reasoning, machine learning, natural language processing...
  • By contrast, how the current developments in AI can be integrated within FCA to help AI researchers solve complex problems in their domain,
  • Which role can be played by FCA in the new trends in AI, especially in ML, XAI, fairness of algorithms, and ``hybrid systems'' combining symbolic and subsymbolic approaches.

Topics of interest

Topics of interest include but are not limited to:

  • Concept lattices and related structures:
    pattern structures, relational structures, distributive lattices.
  • Knowledge discovery and data mining:
    pattern mining, association rules, attribute implications, subgroup discovery, exceptional model mining, data dependencies, attribute exploration, stability, projections, interestingness measures, MDL principle, mining of complex data, triadic and polyadic analysis.
  • Knowledge and data engineering:
    knowledge representation, reasoning, ontology engineering, mining the web of data, text mining, data quality checking.
  • Analyzing the potential of FCA in supporting hybrid systems:
    how to combine FCA and data mining algorithms, such as deep learning for building hybrid knowledge discovery systems, producing explanations, and assessing system fairness.
  • Analyzing the potential of FCA in AI tasks
    such as classification, clustering, biclustering, information retrieval, navigation, recommendation, text processing, visualization, pattern recognition, analysis of social networks.
  • Practical applications
    in agronomy, astronomy, biology, chemistry, finance, manufacturing, medicine...

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

Submission details

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

  • technical papers not exceeding 12 pages,
  • system descriptions or position papers on work in progress not exceeding 6 pages.

Submissions are via EasyChair at https://easychair.org/conferences/?conf=fca4ai2022

The workshop proceedings will be published as CEUR proceedings (see preceding editions in CEUR Proceedings Vol-2972, Vol-2729, Vol-2529, Vol-2149, Vol-1703, Vol-1430, Vol-1257, Vol-1058, and Vol-939).


July 23, The Workshop Day
Vienna time (GMT+2)

Moscow time: 12:00

Session 1. FCA for XAI
Intrinsically Interpretable Document Classification via Concept Lattices

Eric George Parakal and Sergei O. Kuznetsov

Towards Fast Finding Optimal Short Classifiers

Egor Dudyrev and Sergei O. Kuznetsov

Session 2. Invited Talk: Karell Bertet (Laboratory L3i, University of La Rochelle, France)

FCA, a Step From Lattice Theory to Efficient Pattern Mining Approaches

Lunch Break
Session 3. Extensions and Theory
Can FCA Provide a Framework for Artificial General Intelligence?

Francisco J. Valverde-Albacete, Carmen Peláez-Moreno, Inma P. Cabrera, Pablo Cordero, and Manuel Ojeda-Aciego

Small Overfitting Probability in Minimization of Empirical Risk for FCA-based Machine Learning

Dmitry V. Vinogradov

Framework for Pareto-Optimal Multimodal Clustering

Mikhail Bogatyrev and Dmitry Orlov

Afternoon Break
Session 4. FCA and NLP
Lazy Classification of Underground Forums Messages Using Pattern Structures

Abdulrahim Ghazal and Sergei O. Kuznetsov

Extended presentation: Organizing Contexts as a Lattice of Decision Trees for Machine Reading Comprehension

Boris Galitsky, Dmitry Ilvovsky, and Elizaveta Goncharova

Final Discussion and Wrap Up