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