FCA2VEC: Embedding Techniques for Formal Concept Analysis

DOI10.1007/978-3-030-93278-7_3OpenAlexW2989757497WikidataQ113643168 ScholiaQ113643168MaRDI QIDQ5054975

Maximilian Stubbemann, Dominik Dürrschnabel, Tom Hanika

Publication date: 12 December 2022

Published in: Complex Data Analytics with Formal Concept Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.11496

zbMATH Keywords

word2veccovering relationformal concept analysislink predictionclosed setscomplex datalow dimensional embeddingvector space embedding

Mathematics Subject Classification ID

Knowledge representation (68T30) Computational aspects of data analysis and big data (68T09)

Related Items (1)

FCA2VEC

Uses Software

• UCI-ml
• ConExp
• Scikit
• word2vec
• k-means++
• node2vec
• DeepWalk
• RDF2vec
• Wembedder
• Paper2vec

Cites Work

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• Discovery of optimal factors in binary data via a novel method of matrix decomposition
• From-below approximations in Boolean matrix factorization: geometry and new algorithm
• The role of a synthetic geometry in representational measurement theory
• Ordered direct implicational basis of a finite closure system
• Discovering implicational knowledge in Wikidata
• Measurement structures and linear inequalities
• On Neural Network Architecture Based on Concept Lattices
• Using FCA for Encoding Closure Operators into Neural Networks
• The Transitive Reduction of a Directed Graph

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