论文标题
通过嵌入空间的绝对方向匹配本体论
Ontology Matching Through Absolute Orientation of Embedding Spaces
论文作者
论文摘要
在创建可互操作和链接的打开数据集时，本体匹配是一项核心任务。在本文中，我们探索了一种基于知识图的新型基于结构的映射方法：嵌入要匹配的本体论，并使用称为绝对方向的方法来对齐两个嵌入空间。在该方法旁边，本文使用合成和现实世界数据集提出了第一个初步评估。我们在合成数据的实验中发现该方法在类似结构的图上很好地工作。它比本体论的大小和结构差异更好地处理对齐噪声。
Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedding spaces. Next to the approach, the paper presents a first, preliminary evaluation using synthetic and real-world datasets. We find in experiments with synthetic data, that the approach works very well on similarly structured graphs; it handles alignment noise better than size and structural differences in the ontologies.
