大数据背景下基于本体的知识表示模型的管理方法和技术

O. Novytskyi
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引用次数: 0

摘要

大数据背景下基于本体的知识表示模型是降低跨语义描述方法数据处理复杂性的一种方法。本研究论文旨在概述有效管理基于本体的模型的方法和技术,以改进大数据系统。针对这种情况,将形状约束语言(SHACL)作为信息验证的关键方法。本文还对知识表示系统和推理器进行了研究和回顾。它描述了在大数据背景下基于本体的方法。通过提供的方法和技术对基于本体的知识表示模型进行适当的管理,可以提高数据集成、大数据质量和业务流程集成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methods and techniques for management of ontolodgy-based knowledge representation models in the context of BIG data
Ontology-based knowledge representation models in the context of big data are one way to reduce complexity for data processing across methods of semantic description. This research paper aims at providing an overview of the methods and techniques for efficient management of the ontology-based models that improve big data systems. For this case, the shapes constraint language (SHACL) for information validation was reviewed as the key method. The knowledge representation systems and reasoners are studied and reviewed in the paper as well. It describes approaches based on ontologies in the context of big data. The proper management of ontology-based knowledge representation models through offered methods and techniques brings improved data integration, big data quality, and business process integration.
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