PAIRPQ: An Efficient Path Index for Regular Path Queries on Knowledge Graphs

Abstract

With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. A Regular Path Query (RPQ) allows for retrieving vertex pairs with the paths between them satisfying regular expressions. As an essential type of queries for RDF graphs, RPQs have been attracting increasing research efforts. Since the complexity of RPQs is in polynomial time with respect to the scale of the knowledge graphs, currently, there has been no efficient method to process RPQs on large-scale knowledge graphs. In this paper, we propose a novel indexing solution by leveraging frequent path mining. Unlike the existing RPQ processing methods, our approach makes full use of frequent paths as the basic indexing facility. The frequent paths extracted from data graphs will be indexed to accelerate RPQs. Meanwhile, since no RPQ benchmark available, we create a micro-benchmark on synthetic and real-world data sets. The experimental results show that PAIRPQ improves the query efficiency by orders of magnitude than the state-of-the-art RDF storage engines.