Knowledge graphs have become an essential source of entitycentric
information for modern applications. Today’s KGs have reached
a size of billions of RDF triples extracted from a variety of sources, including
structured sources and text. While this definitely improves completeness,
the inherent variety of sources leads to severe heterogeneity,
negatively affecting data quality by introducing duplicate information.
We present a novel technique for detecting synonymous properties in
large knowledge graphs by mining interpretable definitions of properties
using association rule mining. Relying on such shared definitions, our
technique is able to mine even synonym rules that have only little support
in the data. In particular, our extensive experiments on DBpedia
and Wikidata show that our rule-based approach can outperform stateof-
the-art knowledge graph embedding techniques, while offering good
interpretability through shared logical rules.