Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Mar 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2; 2025: 1.
5 papers.
- 2024BERT4FCA: A Method for Bipartite Link Prediction using Formal Concept Analysis and BERTcited by 2
- 2021On Interpretability and Similarity in Concept-Based Machine Learningcited by 0
- 2023Mining for Unknown Unknownscited by 0
- 2024Machine Learning and Data Analysis Using Posets: A Surveycited by 0
- 2025BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encodercited by 0
- 2025BicliqueEncoder: An Efficient Method for Link Prediction in Bipartite Networks using Formal Concept Analysis and Transformer Encoder6 Mar 2025
- 2024Machine Learning and Data Analysis Using Posets: A Survey3 Apr 2024
- 2024BERT4FCA: A Method for Bipartite Link Prediction using Formal Concept Analysis and BERT13 Feb 2024
- 2023Mining for Unknown Unknowns11 Jul 2023
- 2021On Interpretability and Similarity in Concept-Based Machine Learning25 Feb 2021