What can FCA do for Artificial Intelligence?
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.