Graph-FCA in practice

Abstract

With the rise of the SemanticWeb, more and more relational data are made available in the form of knowledge graphs (e.g., RDF, conceptual graphs). A challenge is to discover conceptual structures in those graphs, in the same way as Formal Concept Analysis (FCA) discovers conceptual structures in tables. Graph-FCA has been introduced in a previous work as an extension of FCA for such knowledge graphs. In this paper, algorithmic aspects and use cases are explored in order to study the feasibility and usefulness of G-FCA. We consider two use cases. The first one extracts linguistic structures from parse trees, comparing two graph models. The second one extracts workflow patterns from cooking recipes, highlighting the benefits of n-ary relationships and concepts.