Formal Concept Analysis: a Structural Framework for Variability Extraction and Analysis
This work addresses the gap for researchers and practitioners in knowledge representation and variability analysis by providing a structured approach to leverage FCA's mathematical properties, though it is incremental as it builds on existing FCA foundations.
The paper tackles the challenge of applying Formal Concept Analysis (FCA) to variability extraction and analysis by identifying and explaining key properties of the framework that are essential for interpreting variability information in conceptual structures.
Formal Concept Analysis (FCA) is a mathematical framework for knowledge representation and discovery. It performs a hierarchical clustering over a set of objects described by attributes, resulting in conceptual structures in which objects are organized depending on the attributes they share. These conceptual structures naturally highlight commonalities and variabilities among similar objects by categorizing them into groups which are then arranged by similarity, making it particularly appropriate for variability extraction and analysis. Despite the potential of FCA, determining which of its properties can be leveraged for variability-related tasks (and how) is not always straightforward, partly due to the mathematical orientation of its foundational literature. This paper attempts to bridge part of this gap by gathering a selection of properties of the framework which are essential to variability analysis, and how they can be used to interpret diverse variability information within the resulting conceptual structures.