可靠的知识图通过强化学习进行事实预测
Fangfang Zhou1, Jiapeng Mi1, Beiwen Zhang1
1School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410083, China.
Visual computing for industry, biomedicine, and art
|November 19, 2023
概括
EvoPath是一种新的强化学习 (RL) 方法,增强了知识图 (KG) 的事实预测. 通过利用实体异质性和后行走机制,它产生更可靠的推理路径,用于准确的三重真实性判断.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 知识表示 知识表示
背景情况:
- 知识图 (KG) 事实预测对于KG完成至关重要.
- 强化学习 (RL) 是KG事实预测的一个常见方法.
- 由于有限的推理路径,现有的RL方法与不可靠的规则信任计算作斗争.
研究的目的:
- 提出EvoPath,一种基于RL的新方法,用于准确的KG事实预测.
- 为了解决计算规则保密度的现有方法的局限性.
- 提高KG事实预测的可靠性和精度.
主要方法:
- 开发了EvoPath,这是一种基于RL的方法,用于KG事实预测.
- 引入了基于实体异质性的新奖励机制,用于有效的推理路径发现.
- 整合了一个行走后的机制,以利用RL期间被忽视的推理路径.
主要成果:
- 通过提供足够的推理路径,EvoPath促进了规则保密性的可靠计算.
- 拟议的机制允许对预测的三倍数的真实性作出精确的判断.
- 实验表明,与现有方法相比,EvoPath可以实现更准确的事实预测.
结论:
- EvoPath显著提高了KG事实预测的准确性.
- 新的奖励和后行走机制是EvoPath成功的关键.
- 这种方法为KG完成提供了更可靠的方法.
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