Scalable Architectures for Distributed Commonsense Knowledge Bases with Real-Time Synchronization and Fault Tolerance

Abstract

Contemporary advances in knowledge-based artificial intelligence demand architectures that can efficiently store, retrieve, and infer over billions of interlinked entities and relations in real time. This work presents a distributed framework that integrates hypergraph-based representations with scalable multilinear tensor decompositions, capable of capturing complex higher-order dependencies without compromising latency or consistency. We propose a hybrid synchronization layer, adapted from Byzantine fault-tolerant protocols, that ensures linearizable updates even under adversarial conditions. A multi-tiered coding strategy employs erasure-correcting codes alongside homomorphic commitments for secure, fault-tolerant storage of knowledge embeddings. Moreover, we introduce a novel semantic inference pipeline based on an alignment procedure that uses continuous transport maps to reconcile updates across geographically dispersed shards. We show that the resulting infrastructure provides sublinear communication overhead relative to the number of nodes and transactions while maintaining high-availability guarantees, even under severe network partitions. Experimental analyses on synthetic and real-world workloads demonstrate a significant boost in both throughput and accuracy compared to conventional graph database systems. By fusing rigorous logical formalisms with advanced differential geometric embeddings, the proposed architecture paves the way for next-generation commonsense and specialized domain knowledge engines. Future directions include the extension of hierarchical attention over hyperbolic manifolds and the exploration of quantum-secured protocols for further resilience and efficiency.