Formal Concept Analysis: a Structural Framework for Variability Extraction and Analysis
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis
Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users. Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA) provide repres…
Combining Formal Concept Analysis and semantic information for building ontological structures from texts : an exploratory study
This work studies conceptual structures based on the Formal Concept Analysis method. We build these structures based on lexico-semantic information extracted from texts, among which we highlight the semantic roles. In ou…
Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data
Current trends in pre-training Large Language Models (LLMs) primarily focus on the scaling of model and dataset size. While the quality of pre-training data is considered an important factor for training powerful LLMs, i…
DiversityA note on the connectedness property of union-free generic sets of partial orders
This short note describes and proves a connectedness property which was introduced in Blocher et al. [2023] in the context of data depth functions for partial orders. The connectedness property gives a structural insight…
Toward Formalizing LLM-Based Agent Designs through Structural Context Modeling and Semantic Dynamics Analysis
Current research on large language model (LLM) agents is fragmented: discussions of conceptual frameworks and methodological principles are frequently intertwined with low-level implementation details, causing both reade…