-------------------------------------------------------------------------------------------------------------------
-- 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
-------------------------------------------------------------------------------------------------------------------
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
-------------------------------------------------------------------------------------------------------------------
-------------------------------------------------------------------------------------------------------------------