# Reasoning on Knowledge Graphs with Debate Dynamics

arXiv: https://arxiv.org/abs/2001.00461 · HTML: https://mlsanity.com/paper/2001.00461/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2020.

## Built on by (most cited first, 16 of 16)

- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [Neural-Symbolic Models for Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/2205.10128/) (2022)
- [A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs](https://mlsanity.com/paper/2206.04798/) (2022)
- [Learning to Give Checkable Answers with Prover-Verifier Games](https://mlsanity.com/paper/2108.12099/) (2021)
- [Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2103.10367/) (2021)
- [GraphRAG under Fire](https://mlsanity.com/paper/2501.14050/) (2025)
- [Learning to Walk with Dual Agents for Knowledge Graph Reasoning](https://mlsanity.com/paper/2112.12876/) (2021)
- [Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering](https://mlsanity.com/paper/2107.06325/) (2021)
- [Adapting Neural Link Predictors for Data-Efficient Complex Query Answering](https://mlsanity.com/paper/2301.12313/) (2023)
- [Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability](https://mlsanity.com/paper/2104.06751/) (2021)
- [The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding](https://mlsanity.com/paper/2109.13392/) (2021)
- [Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2111.10625/) (2021)
- [HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning](https://mlsanity.com/paper/2403.05763/) (2024)
- [Complex Logical Query Answering by Calibrating Knowledge Graph Completion Models](https://mlsanity.com/paper/2410.07165/) (2024)
- [Emulating the Human Mind: A Neural-symbolic Link Prediction Model with Fast and Slow Reasoning and Filtered Rules](https://mlsanity.com/paper/2310.13996/) (2023)
- [Reward Design for Justifiable Sequential Decision-Making](https://mlsanity.com/paper/2402.15826/) (2024)

## Abstract (arXiv)

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.