Hierarchical Memory Networks for Multi-Hop Reasoning in Large-Scale Knowledge Bases

Abstract

Knowledge graph reasoning has emerged as a critical task in artificial intelligence, enabling systems to infer missing information and answer complex queries through multi-hop reasoning. Traditional memory network architectures, while effective for single-hop reasoning tasks, struggle to capture the hierarchical relationships and long-range dependencies inherent in large-scale knowledge bases. This paper proposes a novel Hierarchical Memory Network (HMN) framework that addresses these limitations by introducing a multi-layered memory architecture with hierarchical attention mechanisms. The HMN framework decomposes complex multi-hop reasoning into a structured hierarchical process, where each layer progressively refines the reasoning path by attending to relevant knowledge at different levels of abstraction. Our approach integrates three key innovations: a hierarchical memory organization that explicitly models knowledge at multiple granularities, a progressive attention mechanism that enables iterative refinement of reasoning paths, and a dynamic memory retrieval strategy that efficiently scales to knowledge bases containing millions of entities and relations. Experimental evaluation on multiple benchmark datasets demonstrates that HMN achieves superior performance compared to existing state-of-the-art methods in multi-hop question answering and knowledge graph completion tasks. The hierarchical architecture not only improves reasoning accuracy but also enhances interpretability by providing explicit attention patterns at each reasoning step. Our findings suggest that explicitly modeling hierarchical structures in memory-augmented neural networks is essential for achieving robust multi-hop reasoning in large-scale knowledge-intensive applications.