  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.
