CLARK：知识图谱上自适应推理的闭环学习
CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs
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中文总结 AI 辅助
针对机器学习模型在数据分布变化及整合先验知识方面的局限，CLARK框架在马尔可夫逻辑网络的逻辑程序形式下，整合知识图谱、符号规则挖掘和概率推理，经实验验证可提升分类性能与推理可泛化性，为构建相关模型提供原则性方法。
中文摘要 AI 辅助
机器学习模型广泛用于通过从数据中提取统计模式来自动化分类任务。然而，数据分布变化时其性能会下降，且对整合先验知识支持有限。为解决这些局限，我们提出CLARK框架，它在马尔可夫逻辑网络的逻辑程序形式下整合知识图谱、符号规则挖掘和概率推理。从CACTUS衍生的知识图谱开始，CLARK将图结构转化为LP$^{\text{MLN}}$程序，并用符号学习者提出的候选规则迭代丰富它。这些规则通过概率权重学习校准，实现不确定下的推理和基础图结构的优化。我们在两个医学数据集上评估CLARK，分析规则质量和下游分类性能。结果表明CLARK提高了分类性能和推理的可泛化性。总体而言，CLARK为构建自适应、可解释、知识驱动的分类模型提供了原则性方法。
英文摘要
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 symbolic learners. These rules are calibrated through probabilistic weight learning, enabling reasoning under uncertainty and refinement of the underlying graph structure. We evaluate CLARK on two medical datasets, analysing both rule quality and downstream classification performance. Results demonstrate that CLARK leads to improved classification performance and more generalisable inference. Overall, CLARK provides a principled approach to constructing adaptive, interpretable, knowledge-driven models for classification.
发表机构
- Sano - Centre for Computational Personalised Medicine（萨诺计算个性化医学中心）
- CISUC/LASI, Department of Informatics Engineering, University of Coimbra（科英布拉大学信息工程系CISUC/LASI）
- DeMaCS, University of Calabria（卡拉布里亚大学DeMaCS）
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