Mining Simple Path Traversal Patterns in Knowledge Graph

Abstract

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.

Opportunities and Challenges of Ideological and Political Education for College Students in the Internet Era

“…Entity extraction, relationship extraction, and other techniques that automatically remove disciplinary knowledge entities from the educational domain's knowledge base and establish inter-entity association relationships are crucial to building a knowledge mapping of political and ideological education [17][18]. Figure 1…”