A fine-grained RDF graph model for fuzzy spatiotemporal data

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Soft Computing Pub Date : 2024-08-31 DOI:10.1016/j.asoc.2024.112166
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Abstract

The uncertainty and spatiotemporal dynamics of information necessitate the urgent modeling of fuzzy spatiotemporal knowledge across various applications, with the Resource Description Framework (RDF) serving as a widely recognized data representation model. Existing models suffer from incomplete semantic representation and poor robustness in modeling fuzzy spatiotemporal data, e.g., lack of fuzziness in spatiotemporal semantics; lack of altitude in spatial semantics. Meanwhile, the algebraic framework regarding the model has not been investigated. Thus, in this paper, we first propose a new fine-grained fuzzy spatiotemporal RDF model. This model can represent fine-grained uncertain spatiotemporal semantics that may be associated with any element of a spatiotemporal RDF. We further define its graph algebraic operations. Note that we demonstrate the use of the algebraic operations for fuzzy spatiotemporal RDF querying. Finally, we establish a set of transformation rules for SPARQL query syntax to algebraic operations in fuzzy spatiotemporal RDF. In addition, we used experiments to evaluate the validity and rationality of our model.

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模糊时空数据的细粒度 RDF 图模型
由于信息的不确定性和时空动态性,迫切需要对各种应用中的模糊时空知识进行建模,而资源描述框架(RDF)是一种广为认可的数据表示模型。现有模型存在语义表示不完整、模糊时空数据建模鲁棒性差等问题,如时空语义缺乏模糊性、空间语义缺乏高度等。同时,有关模型的代数框架也尚未研究。因此,本文首先提出了一种新的细粒度模糊时空 RDF 模型。该模型可以表示与时空 RDF 的任何元素相关联的细粒度不确定时空语义。我们进一步定义了它的图代数运算。请注意,我们演示了如何将代数运算用于模糊时空 RDF 查询。最后,我们建立了一套从 SPARQL 查询语法到模糊时空 RDF 中代数运算的转换规则。此外,我们还通过实验来评估我们模型的有效性和合理性。
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来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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