FCA2VEC: Embedding Techniques for Formal Concept Analysis

TL;DR

FCA2VEC introduces embedding techniques for formal concept analysis that enable large data set applications and improve low-dimensional embeddings, maintaining explainability and demonstrating effectiveness on diverse data sets.

Contribution

The paper presents novel FCA-based embedding methods that extend FCA applicability to large data and enhance node2vec for low-dimensional embeddings.

Findings

Effective embedding of large data sets using FCA concepts

Improved low-dimensional embeddings with FCA constraints

Successful evaluation on diverse real-world data sets

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 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.