用大模型自动翻译术语库，让多语言研究更互通。
Mind the Language Gap in Digital Humanities: LLM-Aided Translation of SKOS Thesauri
- 结合外部翻译与大模型精修，提升术语库翻译质量。
- 支持15种语言，显著改善跨语言术语匹配效果。
- 无需专业背景，普通电脑就能运行，适合人文学者使用。
我们提出WOKIE，一个开源、模块化且开箱即用的自动化SKOS术语库翻译流水线。该工作回应数字人文领域中语言多样性带来的知识资源可及性、复用性与语义互操作性瓶颈。WOKIE融合外部翻译服务与大语言模型（LLMs）的针对性优化，在翻译质量、可扩展性与成本间取得平衡。系统设计为可在日常硬件上运行，并易于扩展，无需机器翻译或大模型专业知识。我们在15种语言的多个数字人文术语库上，采用不同参数、翻译服务和大模型对WOKIE进行了评估，系统分析了翻译质量、性能及本体匹配改进情况。结果表明，WOKIE能通过无门槛自动化翻译和提升本体匹配性能，有效增强术语库的可访问性、可复用性与跨语言互操作性，助力构建更包容、多语言的研究基础设施。
原文摘要 · Abstract (English)
We introduce WOKIE, an open-source, modular, and ready-to-use pipeline for the automated translation of SKOS thesauri. This work addresses a critical need in the Digital Humanities (DH), where language diversity can limit access, reuse, and semantic interoperability of knowledge resources. WOKIE combines external translation services with targeted refinement using Large Language Models (LLMs), balancing translation quality, scalability, and cost. Designed to run on everyday hardware and be easily extended, the application requires no prior expertise in machine translation or LLMs. We evaluate WOKIE across several DH thesauri in 15 languages with different parameters, translation services and LLMs, systematically analysing translation quality, performance, and ontology matching improvements. Our results show that WOKIE is suitable to enhance the accessibility, reuse, and cross-lingual interoperability of thesauri by hurdle-free automated translation and improved ontology matching performance, supporting more inclusive and multilingual research infrastructures.
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