论文标题
用有影响力的例子在知识图嵌入模型中解释链接预测
Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples
论文作者
论文摘要
我们研究了在知识图嵌入（KGE）模型中解释链接预测的问题。我们提出了一种基于示例的方法，该方法利用了知识图中节点和边缘的潜在空间表示以解释预测。我们通过观察到有影响力的三元组后，通过观察模型性能的降低来评估了鉴定的三元组的重要性。我们的实验表明，这种生成解释方法的方法在两个公开数据集的KGE模型上的表现优于基准。
We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models. We propose an example-based approach that exploits the latent space representation of nodes and edges in a knowledge graph to explain predictions. We evaluated the importance of identified triples by observing progressing degradation of model performance upon influential triples removal. Our experiments demonstrate that this approach to generate explanations outperforms baselines on KGE models for two publicly available datasets.
