SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models
Researchers propose SC-Taxo, an LLM-driven framework that addresses a persistent weakness in automated taxonomy generation: maintaining semantic coherence across hierarchical levels. Scientific knowledge organization has become a bottleneck as publication volume explodes, and existing systems produce structurally inconsistent hierarchies that undermine downstream applications like trend analysis and knowledge retrieval. This work identifies hierarchical semantic consistency as the core failure mode and builds LLM-based solutions around it, advancing how AI can structure domain knowledge at scale. The approach has implications for knowledge management systems, research discovery platforms, and any application requiring reliable ontology generation.
Modelwire context
ExplainerThe paper isolates hierarchical semantic consistency as a distinct problem from general LLM reasoning. Prior work treated taxonomy generation as a single inference task; SC-Taxo treats it as a multi-level constraint satisfaction problem where parent-child semantic alignment must be explicitly enforced.
This connects directly to the pattern established in recent coverage on procedural execution and validation-driven workflows. Just as 'When LLMs Stop Following Steps' identified step-fidelity as separate from reasoning ability, and 'Generating Statistical Charts' decomposed visualization into validation gates, SC-Taxo decomposes taxonomy generation into hierarchical validation layers. The underlying insight is consistent: LLMs fail not at individual inference but at maintaining coherence across sequential or structural constraints. RunAgent and EGREFINE follow the same pattern, trading some expressiveness for determinism by adding explicit structural guardrails.
If SC-Taxo's hierarchies maintain semantic consistency when evaluated on out-of-domain taxonomies (e.g., trained on biomedical, tested on legal), that confirms the approach generalizes. If performance degrades significantly on new domains, the method may be overfitted to the constraint formulation rather than solving the underlying consistency problem.
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MentionsSC-Taxo · Large Language Models
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