1st Workshop 'What can FCA do for Artificial Intelligence?'
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification, introduced and detailed in the book of Bernhard Ganter and Rudolph Wille, "Formal Concept Analysis", Springer 1999. The area came into being in the mid-1980s and has since then spawned over 1000 scientific publications and a variety of practically deployed tools.
Application domains of FCA include several fields of AI, but also areas as diverse as Psychology, Social Network Analysis, and Software engineering. FCA allows to build from binary data - a binary context with objects in rows and attributes in columns - a taxonomic data structure called concept lattice which can be used for many purposes, especially for Artifcial Intelligence (AI) needs.
Actually, just as "classification" is a polysemic term and a polymorphic activity, so is FCA: it can be regarded as a mathematical theory for classification and data analysis, but also as a very powerful theory for knowledge processing involving learning, knowledge discovery, knowledge representation and reasoning, ontology engineering, and as well information retrieval and text processing...In this way, there exist "natural links" between FCA and AI.
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 and clustering.
- Knowledge engineering and ontology engineering: knowledge representation and reasoning.
- Scalable algorithms for concept lattices and artificial intelligence "in the large" (distributed aspects, big data).
- Applications of concept lattices: semantic web, information retrieval, visualization and navigation, pattern recognition.
The workshop will include time for audience discussion for having a better understanding of the issues, challenges, and ideas being presented.
Marianne Huchard: Relational Concept Analysis: a synthesis and open questions