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Knowledge graphs (KGs) are key tools in many AI-related tasks such asreasoning or question answering. This has, in turn, propelled research in linkprediction in KGs, the task of predicting missing relationships from theavailable knowledge. Solutions based on KG embeddings have shown promisingresults in this matter. On the downside, these approaches are usually unable toexplain their predictions. While some works have proposed to compute post-hocrule explanations for embedding-based link predictors, these efforts havemostly resorted to rules with unbounded atoms, e.g., bornIn(x,y) =>residence(x,y), learned on a global scope, i.e., the entire KG. None of theseworks has considered the impact of rules with bounded atoms such asnationality(x,England) => speaks(x, English), or the impact of learning fromregions of the KG, i.e., local scopes. We therefore study the effects of thesefactors on the quality of rule-based explanations for embedding-based linkpredictors. Our results suggest that more specific rules and local scopes canimprove the accuracy of the explanations. Moreover, these rules can providefurther insights about the inner-workings of KG embeddings for link prediction.

Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings