Study of Hadoop-Based Semantic Big Data Distributed Spectrum Clustering Method
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DOI: 10.25236/dpaic.2018.034
Author(s)
Gang Chen, Dawei Zhao
Corresponding Author
Dawei Zhao
Abstract
With the continuous development of the Sematic Web and the associated dataset project, semantic data in various fields is being expanded on a large scale. At the same time, there is a complex semantic correlation between these large-scale semantic data. The mining of these related information is of great significance to researchers. In order to solve the problems of traditional computing engine's computational performance and scalability in the large-scale semantic data reasoning, a Hadoop-based semantic big data distributed reasoning framework is proposed, and the corresponding property chain is designed. Prototype reasoning system to efficiently discover potentially valuable information in massive semantic data. The experiment mainly focuses on the semantic association discovery between the ontology in the medical and life sciences. The experimental results show that the inference system has achieved good performance--expandability and accuracy.
Keywords
Hadoop-based Semantic, Big Data, Spectrum Clustering