LKAQ: Large-scale knowledge graph approximate query algorithm

Abstract

The problems of storing and processing queries for knowledge graphs (KGs) have always been a hot topic in the database community. Various tools, for example, 3store, RDF-3X, Jena2, and gStore, have been proposed. Recently, KGs have gradually shown a non-strict structure, and their volumes continue to grow. As a result, current KG storage and query tools cannot handle the intricate relationships in KGs or support massive data in limited memory space. In addition, an increasing number of users want to use KGs under limited computing resources. Therefore, to meet the current needs of KGs and solve the above problems, we propose a large-scale knowledge graph approximate query algorithm (LKAQ) adopting the idea of an approximate query processing algorithm. LKAQ gives users the ability to control the trade-off among query time, accuracy, and in-memory usage. From extensive experiments, we demonstrate that LKAQ outperforms state-of-the-art approaches with memory constraints.