WLeidenRDF: RDF Data Query Method based on Semantic-Enhanced Graph-Clustering Algorithm
Yang Liu, Zhou Chen, Yiqing Feng, Zhifang Liao, Zhigang Hu, Jun Bo Long · 2020
Graph-clustering algorithms are designed to split large-scale Resource Description Framework (RDF) graphs into subgraphs to improve RDF query performance. However, triple semantics and graph structures embedded in RDF graphs are often ignored during the RDF graph partition. Accordingly, this study utilizes the Leiden algorithm for uncovering community structure to cluster RDF graphs. We propose an optimized WLeidenRDF algorithm to uncover RDF communities and cluster vertex with strong semantics. Different weights are set to predicates in RDF triples to identify semantic-relevance degree and ensure semantic connections after RDF clustering and segmentation. The experiments on WatDiv data sets demonstrate that our WLeidenRDF algorithm obtains better partitions in accordance with modularity than those of the Leiden algorithm. We implement our algorithm on Presto distributed SQL query engine. Experimental results indicate that our algorithm can substantially reduce query time by clustering RDF graphs than with other RDF query methods.