Datalog Reasoning

Datalog reasoning is a powerful logic-based approach to querying and inferencing knowledge represented as facts and rules. Current research focuses on improving the efficiency of Datalog engines through optimized storage, novel algorithms like Boolean matrix manipulation, and the integration of hypertree decompositions for complex rule sets. This work addresses challenges in scalability, explainability (particularly concerning the soundness of rules learned by neural networks), and the incorporation of uncertainty and arithmetic into Datalog, impacting applications ranging from knowledge graph completion and IoT reasoning to industrial data analytics. The ultimate goal is to enable faster, more efficient, and more explainable reasoning over large and complex datasets.