Abstract
With the rise of the Semantic Web, 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.
This research is supported by ANR project IDFRAud (ANR-14-CE28-0012-02).
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Notes
- 1.
Empty tuples are covered for the sake of generality but are not used in this paper.
- 2.
Source code, datasets, and concept sets at https://bitbucket.org/sebferre/g-fca.
- 3.
In “Les Fleurs du mal”. Charles Baudelaire. 1857.
- 4.
- 5.
Note that, the extraction of the 284 patterns in the sequential model takes about 4 s and the extraction of the 68 patterns in the composition model takes about 20 s.
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Ferré, S., Cellier, P. (2016). Graph-FCA in Practice. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds) Graph-Based Representation and Reasoning. ICCS 2016. Lecture Notes in Computer Science(), vol 9717. Springer, Cham. https://doi.org/10.1007/978-3-319-40985-6_9
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