Abstract
In this paper, a new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task. Ontologies are represented as graphs, and the translation is performed from a node in the source ontology graph to a path in the target ontology graph. The proposed framework, Truveta Mapper (TM), leverages a multi-task sequence-to-sequence transformer model to perform alignment across multiple ontologies in a zero-shot, unified and end-to-end manner. Multi-tasking enables the model to implicitly learn the relationship between different ontologies via transfer-learning without requiring any explicit cross-ontology manually labeled data. This also enables the formulated framework to outperform existing solutions for both runtime latency and alignment quality. The model is pre-trained and fine-tuned only on publicly available text corpus and inner-ontologies data. The proposed solution outperforms state-of-the-art approaches, Edit-Similarity, LogMap, AML, BERTMap, and the recently presented new OM frameworks in Ontology Alignment Evaluation Initiative (OAEI22), offers log-linear complexity in contrast to quadratic in the existing end-to-end methods, and overall makes the OM task efficient and more straightforward without much post-processing involving mapping extension or mapping repair.
Abstract (translated)
在本文中,我们提出了一种新的视角,用于 unsupervised Ontology Matching (OM) 或 Ontology Alignment (OA),将其视为翻译任务。将主题模型表示为图形,翻译从源主题模型节点到目标主题模型路径进行。我们提出的框架是Truveta Mapper (TM),它利用多任务序列到序列Transformer模型,以零样本、统一和端到端的方式对齐多个主题模型。多任务使模型通过Transfer Learning自动学习不同主题之间的关系,而无需任何明确的跨主题手动标签数据。这还使框架在运行时延迟和对齐质量方面胜过现有的解决方案。模型仅在公共文本库和内部主题数据上进行了预训练和微调。我们提出的解决方案比最先进的方法、编辑相似性、LogMap、AML、BERTMap和最近提出的新OM框架(Ontology Alignment Evaluation Initiative,OAEI22)的新方法在Log-线性复杂性方面表现更好,与现有的端到端方法相比具有quadratic复杂性,同时使整个OM任务高效且更加简单,而无需大量的后期处理涉及映射扩展或映射修复。
URL
https://arxiv.org/abs/2301.09767
