# CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs

arXiv: https://arxiv.org/abs/2607.19996 · HTML: https://mlsanity.com/paper/2607.19996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 14 of 14)

- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Hinge-Loss Markov Random Fields and Probabilistic Soft Logic](https://mlsanity.com/paper/1505.04406/) (2015)
- [The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning](https://mlsanity.com/paper/2402.12715/) (2024)
- [Dissonance Between Human and Machine Understanding](https://mlsanity.com/paper/2101.07337/) (2021)
- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [Weight Learning in a Probabilistic Extension of Answer Set Programs](https://mlsanity.com/paper/1808.04527/) (2018)
- [CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures](https://mlsanity.com/paper/2308.12031/) (2023)
- [LPMLN, Weak Constraints, and P-log](https://mlsanity.com/paper/2506.12784/) (2025)
- [Weighted Rules under the Stable Model Semantics](https://mlsanity.com/paper/2605.09519/) (2026)
- [CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration](https://mlsanity.com/paper/2506.14843/) (2025)

## Abstract (arXiv)

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 (LPMLN) formalism. Starting from CACTUS-derived KGs, CLARK translates graph structure into an LPMLN 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.