Graph-Enhanced Prompt Tuning for Evidence-Grounded HFACS Classification in Power-System Safety

Wenhua Zeng (), Wenhu Tang (), Diping Yuan, Bo Zhang, Na Xu and Hui Zhang
Additional contact information
Wenhua Zeng: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Wenhu Tang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Diping Yuan: Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518057, China
Bo Zhang: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Na Xu: School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Hui Zhang: Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518024, China

Energies, 2025, vol. 18, issue 20, 1-36

Abstract: Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limited fine-grained recognition, inconsistent label paths, and weak evidence traceability. We propose EG-HPT (Evidence-Grounded Hierarchy-Aware Prompt Tuning), which augments hierarchical prompt tuning with Global Pointer-based nested-entity recognition and a sentence–entity heterogeneous graph to aggregate cross-sentence cues; label-aware attention selects Top- k evidence nodes and a weighted InfoNCE objective aligns label and evidence representations, while a hierarchical separation loss and an ancestor-completeness constraint regularize the taxonomy. On a HFACS-based power-accident corpus, EG-HPT consistently outperforms strong baselines in Micro-F1, Macro-F1, and path-constrained Micro-F1 (C-Micro-F1), with ablations confirming the contributions of entity evidence and graph aggregation. These results indicate a deployable, interpretable solution for automated risk factor analysis, enabling auditable evidence chains and supporting multi-granularity accident intelligence in safety-critical operations.

Keywords: hierarchical text classification; prompt tuning; heterogeneous graphs; power-system accidents; HFACS; explainability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/20/5389/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/20/5389/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:20:p:5389-:d:1770134

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().