摘要
随着大规模语义数据的涌现,研究高效的并行化语义推理成为热点问题之一。现有推理框架大多存在可扩展性方面的不足,难以满足大规模语义数据的需求。针对现有推理框架的不足,提出一种基于Spark的大规模语义数据分布式推理框架。该框架主要包括语义建模、规则提取和基于Spark的并行推理机等3个模块。通过过程分析和推理实例验证,提出的分布式并行推理的计算性能(T(n)=O(log_(2)n))远远优于顺序式推理的计算性能(T(n)=O(n))。
With the emergence of large-scale semantic data,the study of efficient parallel semantic reasoning has already become a hot topic.In terms of scalability,most of the existing reasoning frameworks still have deficiencies,so it is hard to meet the needs of large-scale semantic data.To solve this problem,a distributed reasoning framework for large-scale semantic data based on Spark was proposed in this paper,which is composed of 3 modules,including semantic modeling,rule extraction and Spark-based parallel reasoning.The result of the process analysis and reasoning instance reveals that computing performance of the proposed distributed parallel reasoning(T(n)=O(log_(2)n)) is far better than that of sequential reasoning(T(n)=O(n)).
出处
《计算机科学》
CSCD
北大核心
2016年第S2期93-96,共4页
Computer Science
基金
国家自然科学基金项目(61371090)
辽宁省自然科学基金项目(2015020017)
大连外国语大学校级科研项目(2014XJQN09)资助
