Framework for Using AI to Generate Human-Readable Names for Formal Concept Analysis Structures
A new paper on arXiv introduces a configurable framework that uses large language models to assign meaningful names to abstract concepts generated by Formal Concept Analysis and Relational Concept Analysis. These knowledge-extraction methods produce formally rigorous but often opaque symbolic structures that domain experts struggle to interpret. The framework aims to bridge the gap between machine-generated abstractions and human-readable knowledge by making explicit the semantic choices involved in naming.
Researchers have proposed a variability-based framework for using large language models to name concepts produced by Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA), two established methods for extracting structured knowledge from symbolic data. While FCA and RCA generate explicit conceptual structures, implications, and relational dependencies, the resulting concepts are typically identified by technical labels that limit their usability for domain experts. The paper characterizes key linguistic and terminological challenges in naming such abstractions, including ambiguity, discrimination, concision, and consistency across related concepts. The core contribution is a configurable variability model that controls which sources of information—such as intent, extent, inherited properties, neighboring concepts, and relational attributes—are exposed to an LLM during the naming process. This design makes the semantic choices involved in moving from formal descriptions to human-readable names transparent and adjustable. The approach is demonstrated as a proof of concept on a small relational dataset in the pizzeria domain, showing how different configurations influence LLM-suggested names and can surface interpretation choices or modeling issues in the underlying data. The work is positioned as a step toward making symbolic knowledge representation more accessible and reusable by non-technical domain experts.
What's missing
The paper is presented only as a proof of concept on a small, single-domain dataset (pizzeria), leaving open questions about scalability to large or complex ontologies, generalizability across domains, robustness to different LLMs, and whether LLM-suggested names are consistently validated as meaningful by actual domain experts. No formal user study or quantitative evaluation of naming quality is reported.
What different sources said
- arXiv cs.AICenter
A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis
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