A Systematic Review of Topological simplification methods for large-scale knowledge graphs: Methods, Architectures, and Future Research Directions

Abstract

Large-scale knowledge graphs have become foundational to modern data-driven systems, enabling semantic reasoning, intelligent search, and advanced analytics across domains such as healthcare, finance, and software engineering. However, their rapid growth introduces significant challenges related to computational complexity, storage overhead, and reasoning inefficiency. Topological simplification methods have emerged as critical techniques for reducing structural complexity while preserving semantic integrity. This paper presents a systematic review of topological simplification approaches for large-scale knowledge graphs, focusing on methods, architectures, and emerging research directions between 2018 and 2025. The study examines graph sparsification, node and edge pruning, hierarchical abstraction, embedding-based reduction, and AI-driven simplification frameworks. Findings indicate a shift from heuristic-based reduction to learning-driven and hybrid architectures integrating graph neural networks and generative AI. The review identifies key contributions such as improved scalability, enhanced query efficiency, and better compression ratios, while also highlighting limitations including semantic loss, evaluation challenges, and lack of standardized benchmarks. This work contributes a comprehensive synthesis of 30 studies, a comparative analysis framework, and future research directions emphasizing AI-assisted topology optimization, secure graph compression, and integration with modern software engineering pipelines.