Embedding-Assisted Entity Resolution for Knowledge Graphs

Daniel Obraczka, Jonathan Schuchart, Erhard Rahm

Keywords: Knowledge Graph, Knowledge Graph Embedding, Entity Resolution

TL;DR: We discover, that Knowledge Graph Embeddings in combination with attribute similarities can significantly improve Entity Resolution results

Abstract:

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 embeddings for 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 (\textit{E}mbedding-\textit{A}ssisted Knowledge \textit{G}raph \textit{E}ntity \textit{R}esolution) 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.