通过上下文向量投影实现多语言知识图谱语义对齐，提升跨语言实体匹配效果。
Semantic Alignment of Multilingual Knowledge Graphs via Contextualized Vector Projections
- 用微调的多语言Transformer生成上下文丰富的嵌入表示
- 基于余弦相似度匹配并阈值筛选，达71% F1（recall 78%，precision 65%）
- 在OAEI-2022评测中较最优基线提升16%，适合多语言知识融合场景
本文提出一种基于嵌入的跨语言本体对齐系统，利用上下文增强的描述生成技术丰富实体语义。采用微调的多语言Transformer模型生成更优嵌入表示，通过余弦相似度匹配候选实体对，并使用阈值过滤保留高相似度对。在OAEI-2022多农场赛道上评估，取得71% F1分数（召回率78%，精确率65%），相较最优基线提升16%，表明所提对齐流程能有效捕捉细微的跨语言语义相似性。
原文摘要 · Abstract (English)
The paper presents our work on cross-lingual ontology alignment system which uses embedding based cosine similarity matching. The ontology entities are made contextually richer by creating descriptions using novel techniques. We use a fine-tuned transformer based multilingual model for generating better embeddings. We use cosine similarity to find positive ontology entities pairs and then apply threshold filtering to retain only highly similar entities. We have evaluated our work on OAEI-2022 multifarm track. We achieve 71% F1 score (78% recall and 65% precision) on the evaluation dataset, 16% increase from best baseline score. This suggests that our proposed alignment pipeline is able to capture the subtle cross-lingual similarities.
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