Workshop of the IJCAI 2015 conference

4th Workshop 'What can FCA do for Artificial Intelligence?'

25 July 2015, Workshop of the IJCAI 2015 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 involving learning, knowledge discovery, knowledge representation and reasoning, ontology engineering, and as well as information retrieval 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, both from the data analysis and knowledge discovery point of view and from the knowledge representation point of view, including, e.g., ontology engineering. 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 two main issues:

How can FCA support AI activities such as knowledge processing (knowledge discovery, knowledge representation and reasoning), learning (clustering, pattern and data mining), natural language processing, information retrieval.
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 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.