# Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

arXiv: https://arxiv.org/abs/1703.02819 · HTML: https://mlsanity.com/paper/1703.02819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [BERT4FCA: A Method for Bipartite Link Prediction using Formal Concept Analysis and BERT](https://mlsanity.com/paper/2402.08236/) (2024)
- [On Interpretability and Similarity in Concept-Based Machine Learning](https://mlsanity.com/paper/2102.12723/) (2021)
- [Mining for Unknown Unknowns](https://mlsanity.com/paper/2307.05071/) (2023)
- [Machine Learning and Data Analysis Using Posets: A Survey](https://mlsanity.com/paper/2404.03082/) (2024)
- [BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encoder](https://mlsanity.com/paper/2503.07645/) (2025)

## Abstract (arXiv)

This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.