KG-APC: Knowledge Graph-Guided Adaptive Prototype Correction for Few-Shot Entity Recognition in Industrial Maintenance Information Systems

Résumé fourni par la source

Industrial maintenance and fault-diagnosis systems generate textual records, such as maintenance work orders, service requests, causal analyses, and troubleshooting solutions. These records contain domain-specific named entities that provide valuable knowledge for intelligent monitoring, fault diagnosis, maintenance decision support, and industrial knowledge graph construction. However, in practical industrial environments, maintenance records are strongly associated with specific equipment types, production processes, fault modes, and enterprise-specific terminology. As a result, entity schemas vary across systems, new entity types emerge with equipment updates, and high-quality annotation requires substantial domain expertise. These factors make it difficult to obtain sufficient labeled samples for each industrial entity type. Under such low-resource conditions, conventional supervised named entity recognition (NER) models tend to suffer from unstable entity boundary detection and biased entity representations. To address these challenges, this paper proposes a boundary-aware knowledge graph-guided adaptive prototype correction framework for few-shot NER in industrial maintenance information systems. The proposed framework first introduces a boundary-aware span detection mechanism to improve entity localization in noisy and irregular maintenance texts. A knowledge graph-guided adaptive prototype correction module is then designed to construct entity class prototypes from limited support examples, reducing prototype bias caused by sparse annotations. Experiments are conducted on two representative industrial datasets, MaintIE and CFDK, covering maintenance short texts and fault-diagnosis records. Experimental results show that the proposed framework achieves an average Micro-F1 improvement of 1.89 percentage points over the strongest compared baseline across 12 episodic settings on the two industrial datasets: three MaintIE coarse-grained settings, six MaintIE fine-grained settings, and three CFDK settings. The ablation and sensitivity analyses further indicate that boundary-aware span modeling and KG-guided prototype correction jointly contribute to low-resource entity classification. This study provides a data-efficient information extraction solution for AI-enabled industrial knowledge acquisition, fault diagnosis, and maintenance decision support.

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