A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis

2026-06-07

AI Summary

This study addresses the challenge that concepts generated by Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA) often rely on technical labels lacking human-interpretable semantic names, thereby hindering domain experts’ understanding and reuse of knowledge. To overcome this limitation, the work proposes a configurable framework that models concept naming as a controlled variability problem, integrating variability modeling with large language models (LLMs). By explicitly governing the exposure of multi-source semantic cues—such as intent, extent, inheritance, and neighboring concepts—the framework generates interpretable names that simultaneously capture intensional meaning and relational context. Empirical evaluation on a pizza restaurant relational dataset demonstrates that the approach produces diverse, semantically coherent concept names, effectively supporting concept interpretation, knowledge validation, and diagnostic assessment of symbolic data quality.

Abstract

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 representative settings for this issue: they generate explicit conceptual structures, implications, and relational dependencies from object descriptions and relations. Although these structures are explainable by design, their concepts are often identified by technical labels, which limits their use as human-interpretable knowledge units. Assigning meaningful names to such concepts is therefore a key issue for interpretation, navigation, validation, and reuse by domain experts. This paper investigates concept naming in FCA and RCA from a symbolic knowledge representation perspective. We first characterize the linguistic and terminological challenges involved in naming generated symbolic abstractions, including ambiguity, discrimination, concision, and consistency across related concepts. We then propose a configurable framework for LLM-assisted concept naming. The framework relies on a variability model that controls which sources of information are exposed during naming, such as intent, extent, inherited information, neighboring concepts, implications, and relational attributes. It thereby makes explicit the semantic choices involved in moving from formal concept descriptions to human-readable names. The approach is illustrated as a proof of concept on a small relational dataset in the pizzeria domain. This illustration shows how different configurations influence the names suggested by an LLM, and how naming variability can reveal interpretation choices, relational dependencies, and possible modeling issues in the underlying symbolic data.

Problem

• concept naming
• Formal Concept Analysis
• Relational Concept Analysis
• interpretability
• symbolic knowledge

Innovation

• variability-based naming
• Formal Concept Analysis
• Relational Concept Analysis
• LLM-assisted interpretation
• symbolic knowledge representation