基于语义增强标注和推理的关联数据语义扩展

Pu Li, Zhifeng Zhang, Lujuan Deng, Ma Junxia, Wu Fenglong, Gu Peipei, Yuncheng Jiang
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引用次数: 0

摘要

关联数据是由RDF描述的一种新的知识表示和发布形式,它可以提供更精确和可理解的语义结构。然而,当前的RDF Schema (RDFS)和基于sparql的查询策略不能完全表达RDF的语义,因为它们不能释放链接实体之间的隐式语义,因此它们不能释放关联数据的潜力。为了填补这一空白,本章首先定义了一种新的语义标注和推理方法,该方法可以从不同的属性中扩展更多的隐式语义,并提出了一种新的通用关联数据源语义扩展方案,以实现对目标关联数据源的语义扩展。此外,为了在语义数据检索过程中有效地返回更多的信息,我们设计了一个扩展SPARQL模式的新的查询模型。最后,实验结果表明,我们的建议比初始关联数据源具有优势,并且可以返回比一些最具代表性的相似度搜索方法更有效的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic Extension for the Linked Data Based on Semantically Enhanced Annotation and Reasoning
Linked Data, a new form of knowledge representation and publishing described by RDF, can provide more precise and comprehensible semantic structures. However, the current RDF Schema (RDFS) and SPARQL-based query strategy cannot fully express the semantics of RDF since they cannot unleash the implicit semantics between linked entities, so they cannot unleash the potential of Linked Data. To fill this gap, this chapter first defines a new semantic annotating and reasoning method which can extend more implicit semantics from different properties and proposes a novel general Semantically-Extended Scheme for Linked Data Sources to realize the semantic extension over the target Linked Data source. Moreover, in order to effectively return more information in the process of semantic data retrieval, we then design a new querying model which extends the SPARQL pattern. Lastly, experimental results show that our proposal has advantages over the initial Linked Data source and can return more valid results than some of the most representative similarity search methods.
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