大规模模糊RDF数据管理关键技术研究

With extensive applications of Web 2, massive RDF data are emerging. How to represent and process large-scale RDF data effectively and efficiently has being received more attention. Being the current standard model of representing data resources, RDF has being applied in various domains. But RDF cannot fully satisfy diverse requirements from real applications because their specialties. It is hereby necessary to enhance RDF capability in semantic expression. In order to deal with uncertain data which extensively exit in data-intensive and knowledge-intensive application domains, fuzzy RDF data representation and processing has been a crucial research topic in the area of RDF data management. The proposed project investigates how to represent and process large-scale fuzzy RDF data. The major issues are involved in several key techniques for fuzzy RDF data management, including fuzzy RDF model, automatic constructions of fuzzy RDF model with structured fuzzy data resources, fuzzy RDF stores in fuzzy databases. Some original results of these key techniques will be obtained. As a result, the existing theories of traditional RDF data management and fuzzy data management are enriched and evolved, and a solid foundation can be put for constructing Web-based intelligent information systems.

Web2.0技术的广泛使用，导致海量RDF数据的出现，有效处理大规模RDF数据已经受到国内外研究者的高度重视。作为当前数据资源表示的标准模型，RDF在众多领域已得到广泛应用，但具体应用领域特殊性使得它还不能充分满足实际应用多样性的需求，增强RDF语义表达能力成为必然。为了处理现实中广泛存在的不确定信息，模糊数据处理成为了RDF数据管理的一项重要内容。项目致力于大规模模糊数据RDF表示与处理的研究，研究内容覆盖与模糊RDF数据管理相关的若干关键技术：模糊RDF模型；基于结构化模糊数据源的模糊RDF模型自动构建；基于模糊数据库的模糊RDF模型存储。通过在这些关键技术上取得系列原创性研究成果，将丰富和发展经典RDF和模糊数据管理的理论，为Web 环境下智能信息系统开发奠定坚实基础。

Web2.0技术的广泛使用，导致海量RDF数据的出现，有效处理大规模RDF数据已经受到国内外研究者的高度重视。作为当前数据资源表示的标准模型，RDF在众多领域已得到广泛应用，但具体应用领域特殊性使得它还不能充分满足实际应用多样性的需求，增强RDF语义表达能力成为必然。为了处理现实中广泛存在的不确定信息，模糊数据处理成为了RDF数据管理的一项重要内容。项目致力于大规模模糊数据RDF表示与处理的研究，研究内容覆盖与模糊RDF数据管理相关的若干关键技术：模糊RDF模型；基于结构化模糊数据源的模糊RDF模型自动构建；基于模糊数据库的模糊RDF模型存储。通过在这些关键技术上取得系列原创性研究成果，将丰富和发展经典RDF和模糊数据管理的理论，为Web 环境下智能信息系统开发奠定坚实基础。