Optimal Subgraph Matching Queries over Distributed Knowledge Graphs Based on Partial Evaluation.

The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there exists a drawback of partial evaluation, that if the volume of intermediate results is large, assembly computation which is handled by the master would be a bottleneck. In this paper, we propose an efficient assembly algorithm by exploring the characteristics of intermediate results generated during the partial matching stage in each fragment. (1) The partial matching index (PM-Index) structure are constructed by utilizing the incoming and outgoing vertices in parallel to improve the searching efficiency of the assembling phase. (2) The time and space complexity of PM-Index construction are analysed. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.