  A common solution to the semantic heterogeneity problem is to perform
knowledge graph (KG) extension exploiting the information encoded in one or
more candidate KGs, where the alignment between the reference KG and candidate
KGs is considered the critical procedure. However, existing KG alignment
methods mainly rely on entity type (etype) label matching as a prerequisite,
which is poorly performing in practice or not applicable in some cases. In this
paper, we design a machine learning-based framework for KG extension, including
an alternative novel property-based alignment approach that allows aligning
etypes on the basis of the properties used to define them. The main intuition
is that it is properties that intentionally define the etype, and this
definition is independent of the specific label used to name an etype, and of
the specific hierarchical schema of KGs. Compared with the state-of-the-art,
the experimental results show the validity of the KG alignment approach and the
superiority of the proposed KG extension framework, both quantitatively and
qualitatively.
