Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories. In this paper, we link knowledge graph completion and counterfactual reasoning via our new task CFKGR. We model the original world state as a knowledge graph, hypothetical scenarios as edges added to the graph, and plausible changes to the graph as inferences from logical rules. We create corresponding benchmark datasets, which contain diverse hypothetical scenarios with plausible changes to the original knowledge graph and facts that should be retained. We develop COULDD, a general method for adapting existing knowledge graph embeddings given a hypothetical premise, and evaluate it on our benchmark. Our results indicate that KGEs learn patterns in the graph without explicit training. We further observe that KGEs adapted with COULDD solidly detect plausible counterfactual changes to the graph that follow these patterns. An evaluation on human-annotated data reveals that KGEs adapted with COULDD are mostly unable to recognize changes to the graph that do not follow learned inference rules. In contrast, ChatGPT mostly outperforms KGEs in detecting plausible changes to the graph but has poor knowledge retention. In summary, CFKGR connects two previously distinct areas, namely KG completion and counterfactual reasoning.

翻译：知识图谱嵌入（KGEs）最初旨在推断不完整知识库中真实但缺失的事实。本文通过新任务CFKGR，将知识图谱补全与反事实推理联系起来。我们将原始世界状态建模为知识图谱，假设情境建模为图中添加的边，并将图中合理解释的变化建模为基于逻辑规则的推断。我们创建了相应的基准数据集，其中包含多样化的假设情境，涉及对原始知识图的合理变化以及应保留的事实。我们提出COULDD，这是一种在给定假设前提条件下适配现有知识图嵌入的通用方法，并在基准数据集上对其进行了评估。结果表明，KGEs无需显式训练即可学习图中的模式。我们进一步观察到，采用COULDD适配的KGEs能够可靠地检测出遵循这些模式的合理反事实图变化。对人类标注数据的评估显示，采用COULDD适配的KGEs大多无法识别不遵循所学推理规则的图变化。相比之下，ChatGPT在检测图的合理变化方面大多优于KGEs，但知识保留能力较差。总之，CFKGR连接了先前两个独立的研究领域，即知识图谱补全与反事实推理。