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Comprehensive analysis of semantic web reasoners and tools: a survey

机译:语义Web推理机和工具的综合分析:一项调查

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Ontologies are emerging as best representation techniques for knowledge based context domains. The continuing need for interoperation, collaboration and effective information retrieval has lead to the creation of semantic web with the help of tools and reasoners which manages personalized information. The future of semantic web lies in an ontology which describes relationship between terms, and will serve as a foundation for establishing a shared understanding between applications. In this paper, we surveyed and compared numerous reasoning models, ontology tools and express well defined Web services for user with different annotations. We compared latest and traditional reasoners like Pellet, RACER, HermiT, FaCT++ with respect to their features supported by them. Similarly, different variety of ontology development, querying and designing tools like Protege, Jena, SWOOP, Oiled, Apollo, etc. have been compared to predict the inference support through utilizing several features backed up by them. Finally, this paper presents visualized comparison among all reasoners, tools with the aid of their supporting features or characteristics and classified them as strong, average or weak. In addition, we have also classified the reasoner on the basis of their response time and it was observed that Pellet has lowest response time whereas Racer has highest response time.
机译:本体正在成为基于知识的上下文领域的最佳表示技术。对互操作,协作和有效信息检索的持续需求已导致在管理个性化信息的工具和推理机的帮助下创建了语义网。语义网的未来在于描述术语之间关系的本体,它将作为在应用程序之间建立共享理解的基础。在本文中,我们调查并比较了众多推理模型,本体工具,并为带有不同注释的用户表达了定义明确的Web服务。我们比较了最新的和传统的推理机,例如Pellet,RACER,HermiT,FaCT ++,以及它们所支持的功能。同样,已经比较了诸如Protege,Jena,SWOOP,Oiled,Apollo等不同类型的本体开发,查询和设计工具,以通过利用它们支持的若干功能来预测推理支持。最后,本文借助其支持的特征或特性,对所有推理器,工具进行了可视化比较,并将它们分为强,中或弱。此外,我们还根据响应时间对推理机进行了分类,并且观察到Pellet的响应时间最低,而Racer的响应时间最高。

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