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.

通过辩论动态理论提出了一种自动推理知识图谱的新方法，以三段式分类为例，该方法基于增强学习代理和二进制分类器解释和预测关于知识图谱中观点真实性的辩论证据，从而可用于知识图谱的推理和链接预测，实验表明该方法优于其他基线模型。