A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.

翻译：在线共享语义内容的关键步骤是将结构数据源映射成公共域本体学。 这个问题被称作“ 关系- 原子绘图问题 ” ( Rel2Onto) 。 手工模拟数据语义学需要巨大的努力和专门知识。 因此, 学习数据源语义学的自动方法是可取的。 大部分现有工作研究源属性的语义说明。 但是, 自动推断属性之间关系的研究虽然至关重要, 却非常有限。 在本文中, 我们提出了一个新颖的方法, 用于用机器学习、 图形匹配和修改频繁的子绘图挖掘来说明结构数据源, 以修正候选模型。 在我们的工作中, 知识图被作为先前的知识使用。 我们的评估表明, 我们的方法在只了解少量语义模型的棘手案件中, 超越了两种最先进的解决方案。