Matching large and heterogeneous Knowledge Graphs (KGs) has been a challenge in the Semantic Web research community. This work highlights a number of limitations with current matching methods, such as: (1) they are highly dependent on string-based similarity measures, and (2) they are primarily built to handle well-formed ontologies. These features make them unsuitable for large, (semi-) automatically constructed KGs with hundreds of classes and millions of instances. Such KGs share a remarkable number of complementary facts, often described using different vocabulary. Inspired by the role of instances in large-scale KGs, we propose a hybrid matching approach. Our method composes an instance-based matcher that casts the schema matching process as a two-way text classification task by exploiting instances of KG classes, and a string-based matcher. Our method is domain-independent and is able to handle KG classes with unbalanced population. Our evaluation on a real-world KG dataset shows that our method obtains the highest recall and F1 over all OAEI 2020 participants
