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Open Access

Pattern Mining in Linked Data by Edge-Labeling

School of Computer Science and Engineering, Southeast University, Nanjing 210096, China.
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Abstract

Link patterns are consensus practices characterizing how different types of objects are typically interlinked in linked data. Mining link patterns in large-scale linked data has been inefficient due to the computational complexity of mining algorithms and memory limitations. To improve scalability, partitioning strategies for pattern mining have been proposed. But the efficiency and completeness of mining results are still under discussion. In this paper we propose a novel partitioning strategy for mining link patterns in large-scale linked data, in which linked data is partitioned according to edge-labeling rules: Edges are grouped into a primary multi-partition according to edge labels. A feedback mechanism is proposed to produce a secondary bi-partition according to a quick mining process. Local discovered link patterns in partitions are then merged into global patterns. Experiments show that our partition strategy is feasible and efficient.

Keywords

link patternlabelingpartitioningscalability evaluation

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Tsinghua Science and Technology
Volume 21 Issue 2,
April 2016
Pages 168-175
DOI: 10.1109/TST.2016.7442500
Cite this article:
Zhang X, Cheng W. Pattern Mining in Linked Data by Edge-Labeling. Tsinghua Science and Technology, 2016, 21(2): 168-175. https://doi.org/10.1109/TST.2016.7442500

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Received: 25 January 2016
Accepted: 01 March 2016
Published: 31 March 2016
© The author(s) 2016
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