Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
CLARK introduces a novel framework for adaptive reasoning over knowledge graphs by integrating symbolic rule mining and probabilistic reasoning within the Logic Programs with Markov Logic Networks (LP$^{\text{MLN}}$) formalism. This approach addresses the limitations of traditional machine learning models in handling evolving data distributions and incorporating prior knowledge. Evaluation on medical datasets shows that CLARK enhances classification performance and generalizability through iterative enrichment of knowledge graphs with calibrated rules.
CLARK transforms knowledge graphs into adaptive reasoning engines that significantly boost classification accuracy in dynamic environments.
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.