论文标题
与辩论动态的知识图上的推理
Reasoning on Knowledge Graphs with Debate Dynamics
论文作者
论文摘要
我们提出了一种基于辩论动态的知识图自动推理的新方法。主要思想是将三重分类的任务构建为两个强化学习者之间的辩论游戏，这些辩论是提取论证 - 知识图中的路径 - 的目标是促进事实是真实的（论文）或事实是错误的（对立面）。基于这些论点，二进制分类器称为法官，决定了事实是对还是错。两种代理可以被视为稀疏的对抗性发电机，它们为论文或对立面提供了可解释的证据。与其他Black-Box方法相反，这些参数允许用户了解法官的决定。由于这项工作的重点是创建一种可解释的方法来维持竞争性的预测准确性，因此我们将我们的方法基于三重分类和链接预测任务进行基准测试。因此，我们发现我们的方法在基准数据集FB15K-237，WN18RR和Hetionet上的表现优于几个基线。我们还进行了一项调查，发现提取的论点对用户提供了信息。
We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.
