Efficient Search Algorithms Leveraging Inverted Indexing and Parallel Processing for Large-Scale Commonsense Knowledge Repositories

Abstract

This paper investigates advanced search algorithms that leverage inverted indexing techniques and parallel processing paradigms to efficiently query large-scale commonsense knowledge repositories. By focusing on data structures optimized for distributed retrieval and concurrency, we aim to address the escalating demands of real-time, high-volume information access in knowledge-driven systems. Through a detailed exploration of indexing mechanisms designed to decompose textual and symbolic data into token-based entry points, our approach enables near-instant lookup of relevant concepts and relations within massive knowledge bases. We highlight how parallelization strategies—involving thread-level, process-level, and cluster-level execution—augment both query speed and overall throughput. Our discussion further integrates considerations of memory optimization and caching policies that minimize overhead when dealing with highly interconnected commonsense data. We underscore the importance of structured representations and logic-based schemas in guiding index construction, preserving semantic linkages while maintaining computational tractability. This work contributes not only to bridging gaps in retrieval latency but also to improving the scalability of large-scale knowledge infrastructures deployed across diverse application domains, including natural language understanding and automated reasoning. Empirical analysis reveals the performance gains of parallel indexing and searching, demonstrating resilience against growing data volumes and heterogeneous access patterns. Ultimately, these findings provide a robust framework for real-time commonsense retrieval at scale, advancing the capabilities of next-generation intelligent systems.