Abstract
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.
Abstract (translated)
本文介绍了我们关于跨语言本体对齐系统的工作，该系统使用基于嵌入的余弦相似度匹配。为了使本体实体在语境上更加丰富，我们采用了创新技术生成描述，并利用经过微调的多语言转换器模型来生成更好的嵌入表示。通过计算余弦相似度来寻找正向的本体实体对，并随后应用阈值过滤以保留高度相似的实体。我们在OAEI-2022 multifarm轨道上评估了我们的工作，取得了71%的F1分数（召回率为78%，精确率为65%），相比最佳基线得分提高了16%。这表明我们提出的对齐流水线能够捕捉到微妙的跨语言相似性。
URL
https://arxiv.org/abs/2601.00814
