Logic List Mailing Archive

FCA4AI: What can FCA do for AI?

10 Aug 2019
Macao, China

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-- FCA4AI (Seventh Edition) --
``What can FCA do for Artificial Intelligence?''
co-located with IJCAI 2019, Macao, China
August 10 2019
http://www.fca4ai.hse.ru/2019

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General Information.

The six preceding editions of the FCA4AI Workshop (since ECAI 2012 until 
IJCAI 2018) showed that many researchers working in Artificial 
Intelligence are indeed interested by a powerful method for classification 
and mining such as Formal Concept Analysis. This year, we still have the 
chance to organize a new edition of the workshop in Macao co-located with 
the IJCAI 2019 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, knowledge discovery, 
knowledge representation and reasoning, ontology engineering as well as 
information retrieval, recommendation, social network analysis 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, for solving 
more complex problems in data analysis, classification, knowledge 
processing... 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 these main issues:

- How can FCA support AI activities such as knowledge discovery, knowledge 
representation and reasoning, machine learning, natural language 
processing...

- 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, clustering, and biclustering.
- Machine learning: neural networks, random forests, SVM, and combination of classifiers with FCA.
- Knowledge engineering, knowledge representation and reasoning, and ontology engineering (semantic web activities).
- Scalable algorithms for concept lattices and artificial intelligence ``in the large'' (distributed aspects, 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.

IMPORTANT DATES:

Submission deadline: June 8, 2019
Notification to authors: June 29, 2019
Final version: July 15, 2019
Workshop: August 10 2019

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=fca4ai2019

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

WORKSHOP CHAIRS:

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

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