Mining Simple Path Traversal Patterns in Knowledge Graph


  • Feng Xiong Department of Computer Science, Harbin Institute of Technology, China
  • Hongzhi Wang Department of Computer Science, Harbin Institute of Technology, China



Knowledge graph, Path traversal, Data mining


The data mining has remained a subject of unfailing charm for research. The knowledge graph is rising and showing infinite life force and strong developing potential in recent years, where it is observed that acyclic knowledge graph has capacity for enhancing usability. Though the development of knowledge graphs has provided an ample scope for appearing the abilities of data mining, related researches are still insufficient. In this paper, we introduce path traversal patterns mining to knowledge graph. We design a novel simple path traversal pattern mining framework for improving the representativeness of result. A divide-and-conquer approach of combining each path is proposed to discover the most frequent traversal patterns in knowledge graph. To support the algorithm, we design a linked list structure indexed by the length of sequences with handy operations. The correctness of algorithm is proven. Experiments show that our algorithm reaches a high coverage with low output amounts compared to existing frequent sequence mining algorithms.


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Author Biographies

Feng Xiong, Department of Computer Science, Harbin Institute of Technology, China

Feng Xiong received the BEng degree in computer science and technology at Harbin Institute of Technology, China, in 2015. He is now working toward the PhD degree in computer science at Harbin Institute of Technology, China. His research interests include knowledge base, data quality management, data mining, and big data.

Hongzhi Wang, Department of Computer Science, Harbin Institute of Technology, China

Hongzhi Wang is a Professor and doctoral supervisor at Harbin Institute of Technology, ACM member. His research area is data management, including data quality and graph management. He is a recipient of the outstanding dissertation award of CCF and Microsoft Fellow.


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