Authoritative competency frameworks such as ESCO, ROME, and O*NET are essential for aligning education with labor market needs, yet their technical complexity and structural heterogeneity hinder practical adoption by educators. This paper introduces SkillGraph-Service, an interoperable microservice designed to bridge this gap by unifying these resources into a provenance-preserving Knowledge Graph (KG). Adopting a KG-first, LLM-fallback architecture, the system combines symbolic rigor with sub-symbolic flexibility. It implements a lightweight hybrid retrieval engine (fusing SQLite FTS5 and HNSW vector search) to handle the vocabulary mismatch in educator queries, and utilizes Large Language Models (LLMs) strictly for constrained ranking and audience-aware explanation. Empirical evaluation on a multilingual dataset reveals that the proposed hybrid strategy achieves superior retrieval effectiveness (nDCG@5>0.94) with sub-200 ms latency, rendering computationally expensive cross-encoder re-ranking may be unnecessary for this domain. Furthermore, an analysis of generated explanations highlights a trade-off between fluency and faithfulness: while JSON-constrained LLMs ensure high citation precision, deterministic templates remain the most reliable method for maximizing evidence coverage. The resulting architecture offers a practical, scalable, and auditable solution for integrating complex skill data into digital learning ecosystems.

翻译：权威能力框架（如ESCO、ROME和O*NET）对于实现教育与劳动力市场需求对齐至关重要，但其技术复杂性和结构异构性阻碍了教育者的实际采纳。本文提出SkillGraph-Service——一种可互操作的微服务，通过将这些资源统一为保留溯源的知识图谱（KG）来弥合这一鸿沟。系统采用"KG优先、LLM兜底"架构，将符号严谨性与亚符号灵活性相结合。其实现轻量级混合检索引擎（融合SQLite FTS5与HNSW向量检索）以应对教育者查询中的词汇不匹配问题，并严格约束大语言模型（LLM）仅用于受限排序和受众感知解释。基于多语言数据集的实验评估表明，所提混合策略在保持亚200毫秒延迟的同时实现了卓越检索效果（nDCG@5>0.94），表明计算密集型的交叉编码器重排序在该领域可能并非必要。此外，对生成解释的分析揭示了流畅性与忠实度之间的权衡：尽管JSON约束的LLM能确保高引用精度，但确定性模板仍是最大化证据覆盖率的最可靠方法。最终架构为将复杂技能数据集成至数字学习生态系统提供了实用、可扩展且可审计的解决方案。