Details
| Original language | English |
|---|---|
| Title of host publication | Complex Data Analytics with Formal Concept Analysis |
| Publisher | Springer International Publishing AG |
| Pages | 47-74 |
| Number of pages | 28 |
| ISBN (electronic) | 9783030932787 |
| ISBN (print) | 9783030932770 |
| Publication status | Published - 8 Dec 2021 |
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.
Keywords
- Closed sets, Complex data, Covering relation, Formal concept analysis, Link prediction, Low dimensional embedding, Vector space embedding, Word2Vec
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- General Mathematics
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Complex Data Analytics with Formal Concept Analysis. Springer International Publishing AG, 2021. p. 47-74.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - FCA2VEC
T2 - Embedding Techniques for Formal Concept Analysis
AU - Dürrschnabel, Dominik
AU - Hanika, Tom
AU - Stubbemann, Maximilian
N1 - This work is partially funded by the German Federal Ministry of Education and Research (BMBF) in its program “Quantitative Wissenschaftsforschung” as part of the REGIO project under grant 01PU17012, and in its program “Forschung zu den Karrierebedingungen und Karriereentwicklungen des Wissenschaftlichen Nachwuchses (FoWiN)” under grant 16FWN016.
PY - 2021/12/8
Y1 - 2021/12/8
N2 - 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.
AB - 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.
KW - Closed sets
KW - Complex data
KW - Covering relation
KW - Formal concept analysis
KW - Link prediction
KW - Low dimensional embedding
KW - Vector space embedding
KW - Word2Vec
UR - http://www.scopus.com/inward/record.url?scp=85160476406&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1911.11496
DO - 10.48550/arXiv.1911.11496
M3 - Contribution to book/anthology
AN - SCOPUS:85160476406
SN - 9783030932770
SP - 47
EP - 74
BT - Complex Data Analytics with Formal Concept Analysis
PB - Springer International Publishing AG
ER -