CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs
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 structur...
| Authors | Yousef Khan, Luca Gherardini, Marco Maratea et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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