CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs· CLARK：知识图谱上的自适应推理闭环学习
Machine Learning models are widely used for automating classification tasks by extracting statistical patterns from data. However, their performance deteriorates if the data distribution changes, making them ill-suited to handle uncertain and evolving information. Moreover, they provide limited support for integrating prior knowledge. To address these limitations, we present CLARK (Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs), a framework that integrates knowledge graphs, symbolic rule mining, and probabilistic reasoning under the Logic Programs with Markov Logic Networks (LP$^{\text{MLN}}$) formalism. Starting from CACTUS-derived KGs, CLARK translates graph structure into an LP$^{\text{MLN}}$ program and iteratively enriches it with candidate rules proposed by symbol
结合知识图谱与符号规则，提升机器学习模型应对不确定与演化信息的能力。
- 核心方法
- CLARK框架整合了知识图谱、符号规则挖掘和基于Logic Programs with Markov Logic Networks (LP^MLN)的形式化概率推理，通过迭代提出候选规则来丰富初始知识图谱。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 机器学习模型在数据分布变化时性能下降，且难以整合先验知识。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种新的自适应推理方法，能够更好地处理不确定性信息和知识更新。
- 意义与局限
- CLARK为机器学习模型在动态和不确定环境中提供了一个强大的工具，但其依赖于高质量的知识图谱和规则提出算法。