Optimizing Query Processing in Large-Scale Graph-Based Knowledge Bases Using Advanced Traversal Techniques

Abstract

Optimizing query processing in large-scale graph-based knowledge bases is a pivotal concern within data-intensive domains. As the volume and complexity of interconnected datasets grow, systems must contend with both intricate graph topologies and diverse query workloads. This paper addresses the technical challenges encountered in designing and implementing advanced traversal techniques for efficient query resolution. We explore the underlying structures that characterize knowledge bases, emphasizing how entity relationships and dynamic graph properties affect query performance. We propose novel approaches that leverage a combination of logic-driven pruning, index-centric graph partitioning, and adaptive traversal strategies to reduce the time and memory required during query execution. Our discussion highlights the theoretical underpinnings that guide the definition of traversals, as well as methods to integrate logical representations into the query pipeline. We also provide an analytical perspective on cost-effective optimization strategies, showcasing how structured representations and symbolic manipulation can refine and accelerate graph searches. The proposed techniques are empirically evaluated through methodical experiments, illustrating improvements over baseline algorithms in terms of response latency and resource utilization. By merging established graph query paradigms with innovative traversal mechanisms, this paper seeks to offer a comprehensive viewpoint on enhancing large-scale knowledge base performance, thus facilitating more refined and scalable data analytics solutions.