Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (01): 50-55.doi: 10.12052/gdutxb.210043

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A Path Adaptation-based Subgraph Matching Algorithm for Large-scale RDF Graph Data

Hu Xin-miao1, Lin Sui1, Jiang Wen-chao1,2, Xiong Meng2, He Zhong-tang2   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. Cloud Computing Center, Chinese Academy of Sciences, Dongguan 523808, China
  • Received:2021-03-11 Published:2022-01-20

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Abstract: Subgraph pattern matching is the core technology in social network analysis and knowledge discovery, and also the key to determine the accuracy of social network analysis and knowledge discovery. In view of the low accuracy and high cost of the subgraph query algorithm based on backtracking in large-scale data graph environment, a subgraph matching algorithm based on path adaptation is proposed. First, an index based on the path RDF (Resource Description Framework) graph is built. Then, the query graph is decomposed into a set of paths, for each path in the process of decomposition to obtain a set of candidate paths. Finally, the candidate paths are concatenated together through the k-partition intersection graph to build the result subgraph of the query graph. Compared with Spath (Shortest Path) algorithm, Sapper algorithm and SQM (Subgraph Query Matching) algorithm, this algorithm improves the query accuracy by 15% when dealing with large-scale data.

Key words: social network, knowledge discovery, large-scale data graphs, subgraph query, subgraph matching

CLC Number: 

  • TP391
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