Research on Big Data Semantic Index System based on Unsupervised Learning Method of Artificial Neural Network

  • Liya Cai

Abstract

Semantic big data reasoningneeds to efficiently reason real-time and high-speed RDF data. A forward real-time reasoning mechanism based on multi-level index was proposed to solve the problems in semantic big data processing, such as low reasoning efficiency and high query delay. The triggering sequence of reasoning rules was established based on the rule dependence; The most expensive transitive rule was optimized; The result set subgraph index was constructed based on the transitive rule; A triplet index based on variable position was established to eliminate intermediate results; The set that met the condition was obtained by the join operation through the query condition dependency.Then the LUBM data set was used to validate the method, and the experimental results showed that the real-time reasoning method based on multi-level index had obvious advantages over the current real-time reasoning engine in query delay and throughput.

Published
2022-02-28
How to Cite
Liya Cai. (2022). Research on Big Data Semantic Index System based on Unsupervised Learning Method of Artificial Neural Network. Design Engineering, (1), 2078 - 2085. Retrieved from http://thedesignengineering.com/index.php/DE/article/view/9178
Section
Articles