面向分布式数据挖掘优化的基于本体的语义异构度量框架

Bin Liu, Shu-Gui Cao, D. Cao, Qing-Chun Li, Hai-Tao Liu, Shao-Nan Shi
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引用次数: 2

摘要

在分布式数据挖掘(DDM)系统中,数据源之间的语义异构性没有得到普遍的重视,可能会产生影响最终结果质量的潜在风险。本文提出了一种语义距离度量框架,用于提取数据源之间的本质语义异构性。在该框架中,采用基于本体匹配的多策略投票方法,综合综合两个数据源本体在元素级和结构级的语义距离。框架的输出可以作为对数据源进行分组以优化DDM结果的基础。最后,将该框架集成到我们提出的DDM体系结构中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An ontology based semantic heterogeneity measurement framework for optimization in distributed data mining
In distributed data mining (DDM) systems, the semantic heterogeneity between data sources has not got universal attentions, which may produce the potential risks of damaging the quality of the final result. This paper presents a semantic distance measurement framework to extract the essential semantic heterogeneity between data sources. In this framework, an ontology-matching based multi-strategy voting method is utilized to comprehensively synthesize the semantic distances between two data source ontologies in element level and structure level. The output of the framework can be leveraged as the foundation to group the data sources for optimizing the DDM result. Finally, the framework is integrated into a DDM architecture we have proposed.
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