Semantically Enabled Content Convergence System for Large Scale RDF Big Data

Yongju Lee, Hongzhou Duan, Yuxian Sun
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Abstract

The growing number of large scale RDF Big Data raises a challenging data management problem; how should RDF Big Data be stored, queried and integrated. We propose a novel semantic-based content convergence system which consists of acquisition, RDF storage, ontology learning and mashup subsystems. This system serves as a basis for implementing other more sophisticated applications required in the area of Linked Big Data.
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面向大规模RDF大数据的语义支持内容融合系统
随着大规模RDF大数据数量的不断增长,数据管理问题日益严峻。RDF大数据应该如何存储、查询和集成。提出了一种新的基于语义的内容融合系统,该系统由获取子系统、RDF存储子系统、本体学习子系统和mashup子系统组成。该系统可作为实现关联大数据领域所需的其他更复杂应用程序的基础。
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