Automated Knowledge Graph Augmentation and Validation via Multi-Modal Reasoning and HyperScore Assessment

**Abstract:** This paper introduces a novel, fully automated system for augmenting and validating knowledge graphs (KGs) by leveraging multi-modal data ingestion, semantic decomposition, and self-evaluation techniques. Existing KG construction and validation processes often rely heavily on manual curation and are limited by scalability. Our system, based on a tiered evaluation pipeline utilizing logical constraint satisfaction, code execution within a sandbox environment, and rigorous novelty/impact forecasting, significantly reduces human intervention and dramatically increases KG accuracy and comprehensiveness. The key innovation lies in the integration of a dynamically weighted *HyperScore* methodology, allowing the system to prioritize high-confidence, impactful knowledge assertions and iteratively improve its performance through reinforcement learning coupled with expert feedback. This advancement promises to accelerate knowledge discovery across diverse domains and unlock new capabilities in AI reasoning and decision-making.