Automated Knowledge Base Validation & Semantic Drift Mitigation via Multi-Modal Reasoning and HyperScore Calibration

**Abstract:** This paper introduces a novel framework for automated knowledge base (KB) validation and semantic drift mitigation. Traditional KB maintenance methods rely on manual curation or rule-based inconsistencies detection, leading to scalability challenges and delayed responses to evolving information landscapes. Our approach, leveraging a Multi-modal Data Ingestion & Normalization Layer, a Semantic & Structural Decomposition Module, and a Multi-layered Evaluation Pipeline augmented by a HyperScore Calibration system, allows for continuous, automated validation and drift detection. This AI-driven framework identifies and corrects semantic inconsistencies, obsolete information, and emerging trends within the KB with unprecedented accuracy and speed, enabling organizations to maintain knowledge assets vital for informed decision-making. The system is readily deployable and can be adapted to various KB architectures, exhibiting a potential market size of $5B annually within the enterprise knowledge management sector.