What can FCA do for Artificial Intelligence?

General information

The eleven preceding editions of the FCA4AI Workshop (from ECAI 2012 until IJCAI 2023) showed that many researchers working in Artificial Intelligence are indeed interested in powerful techniques for classification and data mining provided by Formal Concept Analysis. The twelfth edition of FCA4AI will once more be co-located with the ECAI Conference, and thus be held in Santiago de Compostela.

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, etc. While the capabilities of FCA are extended, new possibilities are arising in the framework of FCA.

As usual, the FCA4AI workshop is dedicated to the discussion of such issues, and in particular:
- How can FCA support AI activities in knowledge discovery, knowledge representation and reasoning, machine learning, natural language processing and others?
- Vice versa, how can the current developments in AI be adopted within FCA to help AI researchers solve complex problems in their domain?
- Which role can FCA play in the new trends in AI, especially in ML, XAI, fairness of algorithms, and "hybrid systems'' combining symbolic and subsymbolic approaches?

Topics of interest

Topics of interest include but are not limited to:

• Concept lattices and related structures:
  pattern structures, relational structures, distributive lattices.
• Knowledge discovery and data mining:
  pattern mining, association rules, attribute implications, subgroup discovery, exceptional model mining, data dependencies, attribute exploration, stability, projections, interestingness measures, MDL principle, mining of complex data, triadic and polyadic analysis.
• Knowledge and data engineering:
  knowledge representation, reasoning, ontology engineering, mining the web of data, text mining, data quality checking.
• Analyzing the potential of FCA in supporting hybrid systems:
  how to combine FCA and data mining algorithms, such as deep learning for building hybrid knowledge discovery systems, producing explanations, and assessing system fairness.
• Analyzing the potential of FCA in AI tasks
  such as classification, clustering, biclustering, information retrieval, navigation, recommendation, text processing, visualization, pattern recognition, analysis of social networks.
• FCA and Large Language Models (LLMs).
• Practical applications
  in agronomy, astronomy, biology, chemistry, finance, manufacturing, medicine, and others.

The workshop will include time for audience discussion aimed at a better understanding of the issues, challenges, and ideas being presented.

Submission details

The workshop welcomes submissions in pdf format following the CEURART style 1-column
(to be downloaded at https://ceur-ws.org/Vol-XXX/CEURART.zip).

Submissions can be:
- technical papers between 8 and 12 pages,
- system descriptions or position papers describing work in progress not exceeding 6 pages.

Submissions are via EasyChair at https://easychair.org/conferences/?conf=fca4ai2024
The workshop proceedings will be published as CEUR proceedings (see preceding editions in CEUR Proceedings Vol-3489, Vol-3233, Vol-2972, Vol-2729, Vol-2529, Vol-2149, Vol-1703, Vol-1430, Vol-1257, Vol-1058, and Vol-939).