  Embedding large and high dimensional data into low dimensional vector spaces
is a necessary task to computationally cope with contemporary data sets.
Superseding latent semantic analysis recent approaches like word2vec or
node2vec are well established tools in this realm. In the present paper we add
to this line of research by introducing fca2vec, a family of embedding
techniques for formal concept analysis (FCA). Our investigation contributes to
two distinct lines of research. First, we enable the application of FCA notions
to large data sets. In particular, we demonstrate how the cover relation of a
concept lattice can be retrieved from a computational feasible embedding.
Secondly, we show an enhancement for the classical node2vec approach in low
dimension. For both directions the overall constraint of FCA of explainable
results is preserved. We evaluate our novel procedures by computing fca2vec on
different data sets like, wiki44 (a dense part of the Wikidata knowledge
graph), the Mushroom data set and a publication network derived from the FCA
community.
