Reasoning on Knowledge Graphs with Debate Dynamics

Understand

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.