Entity Resolution (ER) is a main task for integrating different knowledge graphs in order to identify entities referring to the same real-world object. A promising approach is the use of graph embeddingsfor ER in order to determine the similarity of entities based on the similarity of their graph neighborhood. Previous work has shown that the use of graph embeddings alone is not sufficient to achieve high ER quality.
We therefore propose a more comprehensive ER approach for knowledge graphs called EAGER (Embedding-Assisted Knowledge Graph Entity
Resolution) to flexibly utilize both the similarity of graph embeddings and attribute values within a supervised machine learning approach and that can perform ER for multiple entity types at the same time. Furthermore, we comprehensively evaluate our approach on 19 benchmark datasets with differently sized and structured knowledge graphs and use hypothesis tests to ensure statistical significance of our results. We also compare our approach with state-of-the-art ER solutions, where EAGER yields competitive results for shallow knowledge graphs but much better results for deeper knowledge graphs.