Category: Workshop

Introduction

While Formal Concept Analysis (FCA) provides a robust theoretical foundation for extracting structures and rules from relational data, its practical adoption requires accessible tools. The fcaR package for R simplifies this process, offering a complete ecosystem to create, manipulate, and visualize formal contexts and implication sets.

In this interactive tutorial, attendees will follow a hands-on approach, running live code to analyze a real-world dataset. Participants will learn step-by-step how to transform raw data into actionable knowledge by extracting concepts and implications. In addition to the core FCA workflow, this session will showcase the latest features and newly implemented algorithms in fcaR, including:

• Seamless data manipulation integrating fcaR and dplyr.
• Conceptual Scaling for complex data types.
• Computation of advanced lattice metrics.
• Working with Bonds between formal contexts.
• Matrix Factorization techniques.
• Mining Causal Association Rules.

The ultimate goal is for participants to gain practical experience, leaving the tutorial ready to apply these computationally efficient data analysis techniques to their own research or production environments.

Organizers

Ángel Mora

Ángel Mora is Associate Professor at the Department of Applied Mathematics in the University of Málaga, Spain. He is also Deputy Director of the School of Computer Engineering for Companies since 2020. He has published more than hundred papers on formal methods based on logic and algebra, with a particular focus on Formal Concept Analysis. In particular, one of the most intensive goals today is the development of a logic of simplifications for implications and rules extracted from data sets, which enable the development of automated methods for reasoning and explaining the results obtained.

He has also developed in collaboration an FCA package called fcaR (see https://fca-malaga.netlify.app/resources/ – over 50M downloads), which is widely used as a tool for solving real-world problems in FCA.

Manuel Ojeda-Hernández

Manuel Ojeda-Hernández is Assistant Professor at the Department of Algebra, Geometry and Topology in the University of Málaga, Spain. His research interest is on algebraic structures under uncertainty, with a particular focus on Formal Concept Analysis and Fuzzy Set Theory. In particular, one of the most intensive goals today is the research on bases of implications with graded attributes both theoretically and computationally in order to make FCA methods applicable in areas with inherent imprecision.

Domingo López Rodríguez

The CONCEPTS 2026 Data Analysis Showcase (CDAS) focuses on discovery, interpretation, and hybrid analysis using tools and methods from the realm of conceptual knowledge structures.

This workshop aims to bridge the gap between theoretical results of structural conceptual analysis, real-world data, and modern machine learning methods. We provide three datasets and challenge you to demonstrate the power of your methods and tools. We particularly encourage hybrid approaches that pair, for example, Formal Concept Analysis (FCA) with sub-symbolic methods such as embeddings, or conceptual visualizations with modern embedding models, or any other interesting combination.

The Datasets

We provide three distinct datasets representing different analytical challenges. Participants are required to provide an analysis for at least one of these datasets, though we encourage submissions that explore multiple datasets.

Dataset A: Geopolitical Evolution (Pure Binary)

• Type: Formal Context (CXT/CSV).
• Source: Extracted from Wikidata, focusing on diplomatic relations and organizational memberships.
• Goal: A “pure” FCA dataset. Uncover geopolitical clusters and identify “conceptually stable” vs. “conceptually volatile” nations.

Dataset B: Global Country Indicators (General/Numerical)

• Type: Multi-valued tabular data (a pre-scaled formal context is also provided).
• Source: Socio-economic and sustainability-related indicators per country, extracted from Wikidata (population, Human Development Index, life expectancy, fertility rate, democracy index, number of official languages, number of diplomatic relations, continent).
• Goal: A general dataset requiring scaling or pattern structures. Identify non-trivial dependencies between development and sustainability indicators and explore how conceptual structures may reveal new insights.

Dataset C: Eurovision Song Contest Results

• Type: Multi-valued tabular data.
• Source: Contest results 1956–2023 (country, performer, song title, placements, and the modern televote/jury point split), derived from the public Eurovision Song Contest Dataset (Spijkervet et al.).
• Goal: Reveal underlying structures and dependencies, in particular with respect to song titles. We encourage using machine learning embeddings to derive categorical features based on semantic similarity.

Submission Formats

We favour modern, contemporary presentation formats. A submission must include the equivalent of at least 2 pages of results/analyses for at least one dataset (in LNCS style), and may be supplemented or presented in full as:

• Scientific Report: A 2–6 page PDF detailing methodology and findings.
• Interactive Blog Post: An online article (e.g., personal blog) featuring interactive visualizations.
• Living Repository: A public Git repository (GitHub/GitLab) containing well-documented Jupyter/Observable notebooks and source code.

Regardless of the format, the submission must clearly describe the methodology, the results, and any hybrid pairings (e.g., FCA + embeddings).

Evaluation and Awards

Submissions are evaluated on a rolling basis. Two special awards will be voted on during the workshop:

• The “Eureka” Award — for the most surprising or impactful insight.
• The “Bridge Builder” Award — for the best integration of conceptual methods with other ML techniques.

Important Dates (2026) — Rolling Process

Date	Milestone
10 June	Release of full datasets.
17 June	Rolling submission window opens. Participants may submit their analysis at any time after this date.
Ongoing	Rolling notifications. Feedback and notification of acceptance within two weeks of each submission.
31 July	Final submission deadline. Last opportunity to submit results for CDAS 2026.
31 August	Workshop session in Montpellier.

Datasets & Submission

Datasets here:
dataset-A-geopolitical-evolution

dataset-B-country-indicators

dataset-C-eurovision

To submit, send your submission via email to cdas@cs.uni-kassel.de using the tag [CDAS 2026 at CONCEPTS] in the subject line and the name of your submission. Attach the PDF, if applicable, or put in the link(s) to your material. Also, please provide a short abstract of about 200 words. If you have any questions, feel free to contact us via the same email address.

Organizers

• Tom Hanika, University of Hildesheim
• Giacomo Kahn, Lumière University Lyon 2