'What can FCA do for Artificial Intelligence?'
The preceding editions of the FCA4AI Workshop (from ECAI 2012 until IJCAI 2019) 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 Santiago de Compostela, co-located with the ECAI 2020 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.
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.
The workshop proceedings will be published as CEUR proceedings (see preceding editions in CEUR Proceedings Vol-2529, Vol-2149, Vol-1703, Vol-1430, Vol-1257, Vol-1058, and Vol-939).