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Regular version of the site
9th Workshop

What Can FCA Do for Artificial Intelligence?

co-located with IJCAI 2021

Call for Papers

-- FCA4AI (Ninth Edition) --
"What can FCA do for Artificial Intelligence?"
co-located with IJCAI 2021, Montréal, Canada
August 21 2021

All events will be held ONLINE

We are pleased to announce that the 9th FCA4AI Workshop co-located with the IJCAI 2021 Conference that will take place in August 2021 as online event via Zoom.

General Information

The preceding editions of the FCA4AI Workshop (from ECAI 2012 until ECAI 2020) 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 a new edition of the workshop in Montréal, co-located with the IJCAI 2021 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 discuss 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 to solve complex problems in their domain.

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, dimensionality reduction, classification, clustering, and biclustering.
  • Pattern mining, subgroup discovery, exceptional model mining, interestingness measures, MDL-based approaches in data mining.
  • Machine learning and hybridization: neural networks, random forests, SVM, and combination of classifiers with FCA.
  • Knowledge engineering, knowledge representation and reasoning, and ontology engineering.
  • Scalable and distributed algorithms for FCA and artificial intelligence, and for mining big data.
  • AI tasks based on FCA: information retrieval, recommendation, social network analysis, data visualization and navigation, pattern recognition.
  • Practical applications in agronomy, biology, chemistry, finance, manufacturing, medicine.

The workshop will include time for audience discussion for having a better understanding 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=fca4ai2021

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

Program of the workshop (UTC/GMT +2 hours)


14.00 Moscow time

Session 1: Theory

(Paris time, Montréal: 7am-9am)

Modelling Conceptual Schemata with Formal Concept Analysis

Uta Priss

Data Overview by means of delta-classes of equivalence. The case of the Titanic dataset

Aleksey Buzmakov, Sergei O. Kuznetsov, Tatiana Makhalova and Amedeo Napoli

FCA Went (Multi-)Relational, But Does It Make Any Difference?

Mickaël Wajnberg, Petko Valtchev, Mario Lezoche, Alexandre Blondin-Massé and Hervé Panetto

Likely-occurring itemsets for pattern mining

Tatiana Makhalova, Sergei O. Kuznetsov and Amedeo Napoli

Session 2: Applications and Data Exploration

(Paris time, Montréal: 9:30am-11am)

Concept-based Chatbot for Interactive Query Refinement in Product Search

Elizaveta Goncharova, Dmitry Ilvovsky and Boris Galitsky

Variability Extraction from Simulator I/O Data Schemata in Agriculture Decision-Support Software

Thomas Georges, Marianne Huchard, Mélanie König, Clémentine Nebut and Chouki Tibermacine

Multimodal Clustering with Evolutionary Algorithms

Mikhail Bogatyrev, Dmitry Orlov and Tatiana Shestaka

Session 3: Extensions

Extensions: New Directions, Machine Learning, Data Mining, Knowledge Representation

(Paris time, Montréal: 11:30am-1:30pm)

On Suboptimality of GreConD for Boolean Matrix Factorisation of Contranominal Scales

Dmitry Ignatov and Alexandra Yakovleva

Summation of Decision Trees

Egor Dudyrev and Sergei O. Kuznetsov

Ensemble Techniques for Lazy Classication Based on Pattern Structures

Ilya Semenkov and Sergei Kuznetsov

A соncept of self-supervised logical rule inference in symbolic classifications

Xenia Naidenova and Vladimir Parkhomenko

Session 4: Knowledge Graphs and Web of Data

(Paris time, Montréal: 2:00pm-3:00pm)

Non-Redundant Link Keys in RDF Data: Preliminary Steps

Nacira Abbas, Alexandre Bazin, Jérôme David and Amedeo Napoli

Formal Concept Analysis for Semantic Compression of Knowledge Graph Versions

Damien Graux, Diego Collarana and Fabrizio Orlandi

Final Discussion and Wrap Up