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 computationally 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.
%0 Book Section
%1 Dürrschnabel2022
%A Dürrschnabel, Dominik
%A Hanika, Tom
%A Stubbemann, Maximilian
%B Complex Data Analytics with Formal Concept Analysis
%C Cham
%D 2022
%E Missaoui, Rokia
%E Kwuida, Léonard
%E Abdessalem, Talel
%I Springer International Publishing
%K 2022 closure2vec fca2vec itegpub myown vector_space_embeddings
%P 47--74
%R 10.1007/978-3-030-93278-7_3
%T FCA2VEC: Embedding Techniques for Formal Concept Analysis
%U https://doi.org/10.1007/978-3-030-93278-7_3
%X 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 computationally 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.
%@ 978-3-030-93278-7
@inbook{Dürrschnabel2022,
abstract = {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 computationally 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.},
added-at = {2023-05-01T18:07:42.000+0200},
address = {Cham},
author = {Dürrschnabel, Dominik and Hanika, Tom and Stubbemann, Maximilian},
biburl = {https://www.bibsonomy.org/bibtex/2cec583ac8e44dca6390b54f6166d5fcf/duerrschnabel},
booktitle = {Complex Data Analytics with Formal Concept Analysis},
doi = {10.1007/978-3-030-93278-7_3},
editor = {Missaoui, Rokia and Kwuida, L{\'e}onard and Abdessalem, Talel},
interhash = {9471d67e393975e5849c497a4aee7b99},
intrahash = {cec583ac8e44dca6390b54f6166d5fcf},
isbn = {978-3-030-93278-7},
keywords = {2022 closure2vec fca2vec itegpub myown vector_space_embeddings},
pages = {47--74},
publisher = {Springer International Publishing},
timestamp = {2024-05-21T10:25:15.000+0200},
title = {FCA2VEC: Embedding Techniques for Formal Concept Analysis},
url = {https://doi.org/10.1007/978-3-030-93278-7_3},
year = 2022
}