Knowledge graphs (KGs) are key tools in many AI-related tasks such as reasoning or question answering. This has, in turn, propelled research in link prediction in KGs, the task of predicting missing relationships from the available knowledge. Solutions based on KG embeddings have shown promising results in this matter. On the downside, these approaches are usually unable to explain their predictions. While some works have proposed to compute post-hoc rule explanations for embedding-based link predictors, these efforts have mostly 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 these works has considered the impact of rules with bounded atoms such as nationality(x,England) => speaks(x, English), or the impact of learning from regions of the KG, i.e., local scopes. We therefore study the effects of these factors on the quality of rule-based explanations for embedding-based link predictors. Our results suggest that more specific rules and local scopes can improve the accuracy of the explanations. Moreover, these rules can provide further insights about the inner-workings of KG embeddings for link prediction.

翻译：知识图谱（KGs）是许多AI相关任务（如推理或问答）中的关键工具。这进而推动了KG中链接预测（即从可用知识中预测缺失关系）的研究。基于KG嵌入的解决方案在此方面展现出令人瞩目的成果。然而，这些方法通常无法解释其预测结果。尽管已有工作提出为基于嵌入的链接预测器计算事后规则解释，但这些研究大多局限于使用无界原子规则（例如 bornIn(x,y) => residence(x,y)），并在全局范围（即整个KG）内学习。尚未有研究考虑有界原子规则（如 nationality(x,England) => speaks(x, English)）或从KG局部区域（即局部范围）学习的影响。因此，我们研究了这些因素对基于嵌入的链接预测器规则解释质量的影响。结果表明，更具体的规则和局部范围能够提升解释的准确性。此外，此类规则还可为KG嵌入在链接预测中的内部机制提供更深入的见解。