Matching place names across writing systems is a persistent obstacle to the integration of multilingual geographic sources, whether modern gazetteers, medieval itineraries, or colonial-era surveys. Existing approaches depend on language-specific phonetic algorithms or romanisation steps that discard phonetic information, and none generalises across script boundaries. This paper presents Symphonym, a neural embedding system which maps toponyms from twenty writing systems into a unified 128-dimensional phonetic space, enabling direct cross-script similarity comparison without language identification or phonetic resources at inference time. A Teacher-Student knowledge distillation architecture first learns from articulatory phonetic features derived from IPA transcriptions, then transfers this knowledge to a character-level Student model. Trained on 32.7 million triplet samples drawn from 67 million toponyms spanning GeoNames, Wikidata, and the Getty Thesaurus of Geographic Names, the Student achieves the highest Recall@1 (85.2%) and Mean Reciprocal Rank (90.8%) on the MEHDIE cross-script benchmark -- medieval Hebrew and Arabic toponym matches curated by domain experts and entirely independent of the training data -- demonstrating cross-temporal generalisation from modern training material to pre-modern sources. An ablation using raw articulatory features alone yields only 45.0% MRR, confirming the contribution of the neural training curriculum. The approach naturally handles pre-standardisation orthographic variation characteristic of historical documents, and transfers effectively to personal names in archival sources, suggesting broad applicability to name resolution tasks in digital humanities and linked open data contexts.

翻译：跨文字系统间地名匹配是整合多语言地理资源（无论是现代地名词典、中世纪行记还是殖民时期勘测记录）时面临的持续性障碍。现有方法依赖特定语言的语音算法或丢弃语音信息的罗马化步骤，且无法跨文字边界泛化。本文提出交响名（Symphonym）——一种将20种文字系统的地名映射到统一128维音素空间的神经嵌入系统，无需在推理时识别语言或使用语音资源即可实现直接的跨文字相似度比较。采用教师-学生知识蒸馏架构，首先从国际音标转录衍生的发音特征中学习，随后将该知识迁移至字符级学生模型。基于从GeoNames、维基数据及盖蒂地名词典的6700万个地名中抽取的3270万条三元组样本训练后，该学生模型在MEHDIE跨文字基准测试（由领域专家策展且完全独立于训练数据的中世纪希伯来语与阿拉伯语地名匹配集）中达到最高召回率@1（85.2%）和平均倒数排名（90.8%），展现出从现代训练材料到前现代跨时间泛化的能力。仅使用原始发音特征的消融实验仅获得45.0% MRR，证实了神经训练课程设计的贡献。该方法能自然处理历史文献特有的标准化前拼写变体，并可有效迁移至档案来源中的人名匹配任务，暗示其在数字人文与关联开放数据环境中具有广泛适用性。