语义知识库构建中的异常数据发现

Discovering Abnormal Data in RDF Knowledge Base

为了提高RDF知识库的数据质量,提出RDF图数据的异常检测及其自动修复的方法。首先,原创性地定义了基于图的条件函数依赖(GCFD),能够将属性值和语义结构的依赖关系统一表示;然后,提出有效的算法框架以及优化策略,挖掘RDF数据中的GCFD,并给出异常数据的自动修复流程;最后,在真实的数据集上,通过大量实验确认解决方案的可行性和优越性。

To effectively improve the data quality of RDF knowledge base, a solution is proposed about abnoraml data discovery and errouneous data repair in RDF graphs. Firstly, the authors innovatively define graph-based conditional functional dependency(GCFD) that can represent the attribute value and semantic structure dependencies of RDF data in a uniform manner. Then, an efficient framework and some novel pruning rules are proposed to discover GCFDs, and the workflow of auto-repairing errorneous data are given. Extensive experiments on several real-life RDF repositories confirm the superiority of proposed solution.