Optimizing subgraph matching over distributed knowledge graphs using partial evaluation

Abstract The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. is implemented on master-slave architecture, endowed with outstanding scalability. However, there are two drawbacks of evaluation: if volume intermediate results large, a large number repeated matches will be generated; computation handled by master would bottleneck. In this paper, we propose an optimal algorithm filter method to reduce exploring computing characteristics framework. (1) An index structure named inner boundary node (IBN-Index) constructed prune graph exploration improve searching efficiency phase. (2) local utilized construct (BN-Index) matches. (3) experimental benchmark datasets show that our approach outperforms state-of-the-art methods.