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2022 | OriginalPaper | Buchkapitel

3. FCA2VEC: Embedding Techniques for Formal Concept Analysis

verfasst von : Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann

Erschienen in: Complex Data Analytics with Formal Concept Analysis

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Conceptual Navigation in Large Knowledge Graphs

Nächstes Kapitel Analysis of Complex and Heterogeneous Data Using FCA and Monadic Predicates

The data was extracted from https://dblp.uni-trier.de/ and is part of the testing data set for the formal concept analysis software conexp-clj, which is hosted at GitHub, see https://github.com/tomhanika/conexp-clj/tree/dev/testing-data.

See https://github.com/tomhanika/conexp-clj.

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Titel: FCA2VEC: Embedding Techniques for Formal Concept Analysis
verfasst von: Dominik Dürrschnabel
Tom Hanika
Maximilian Stubbemann
Verlag: Springer International Publishing
Buch: Complex Data Analytics with Formal Concept Analysis
Print ISBN: 978-3-030-93277-0

Electronic ISBN: 978-3-030-93278-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-93278-7_3

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