Framework for Using AI to Generate Human-Readable Names for Formal Concept Analysis Structures
Researchers propose a configurable framework that uses large language models to assign meaningful names to concepts generated by Formal Concept Analysis and Relational Concept Analysis, addressing a key interpretability challenge in symbolic knowledge extraction. The framework uses a variability model to control which information sources (intent, extent, implications, relational attributes) are exposed during the naming process, making semantic choices explicit. This work matters because it bridges the gap between formally correct but technically labeled conceptual structures and human-interpretable knowledge units needed by domain experts.
Knowledge extraction systems like Formal Concept Analysis (FCA) and Relational Concept Analysis (RCA) generate explicit conceptual structures and dependencies from data, but their concepts are typically identified by technical labels that limit usability by domain experts. This paper addresses concept naming as a key interpretability challenge by proposing an LLM-assisted framework that makes semantic choices explicit through a configurable variability model. The framework allows control over which information sources—such as concept intent, extent, inherited information, neighboring concepts, implications, and relational attributes—are exposed during naming. The authors demonstrate the approach on a pizzeria domain dataset, showing how different configurations influence suggested names and how naming variability can reveal interpretation choices and potential modeling issues. This work contributes to making symbolic knowledge representations more accessible for interpretation, navigation, validation, and reuse by practitioners.
What's missing
The paper is a preprint and does not report evaluation metrics (e.g., user studies, expert validation, or quantitative comparison of naming quality across configurations). The scope is limited to a proof-of-concept on a small dataset, and scalability to larger, more complex domains is not addressed.
What different sources said
- arXiv cs.AICenter
A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis
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