【摘要】 近年来,语义网数据快速增长,适合于处理静态小规模语义数据的前向链语义推理技术暴露出了需对数据进行频繁更新等问题。面对大规模动态语义网数据,对数据更新不敏感的后向链语义推理开始成为新的研究热点。后向链语义推理由查询目标驱动,在查询时根据规则集推理出查询结果。后向链语义推理具有推理过程复杂、规则扩展深度大等特点,在大规模语义数据上推理的效率和可扩展性上有一定的挑战。该文立足于已有的后向链推理技术,详细分析了语义推理规则集的特点,并结合当前主流的大数据处理平台Spark,设计了一套较为高效并且可扩展的大规模并行化语义规则后向链推理系统。该文的主要研究工作分为三个部分:(1)采用预计算本体数据闭包的方法,避免了本体模式在实时推理阶段的重复推理;(2)在后向链语义推理的逆向推理和查询阶段设计了优化措施,进一步提高了推理效率;(3)设计实现了一种基于Spark平台的大规模分布式RDFS/OWL后向链语义推理系统。实验数据显示,该文提出的RDFS/OWL后向链语义推理系统在合成数据集LUBM和真实数据集DBpedia上都表现出了良好的推理性能,在亿条三元组上的推理开销是几秒到几十秒,并且表现出了良好的数据可扩展性和节点可扩展性。更多还原

【Abstract】 With the rapid growth of semantic data in recent years,the backward chaining reasoning method appears to be a new research direction since it is insensitive to data changes.Due to its complex reasoning procedure and large searching space of rule extensions,the backward chaining reasoning is still defected in the reasoning performance and scalability.This paper makes a thorough analysis on the characteristics of semantic rules based upon previous works about backward chaining reasoning technique,and proposes the design of a parallelized large-scale backward chaining reasoning engine of high efficiency and scalability over apache spark,the state-of-art big data processing platform.The main contributions of this paper can be summarized as:1)It avoids duplicate reasoning of terminological patterns during real-time reasoning by pre-calculating terminological closure;2)An optimization methods for reverse reasoning procedure and querying procedure is designed for an improved performance;3)Spark based implementation of the proposed algorithm is presented.Experimental results on both synthetic datasets and real-world datasets show that our method requires only several seconds to ten-seconds of reasoning over hundreds of millions triples,maintaining high data scalability and node scalability as well.更多还原