Abstract: 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.

Key words: RDF data quality, graph-based conditional functional dependencies (GCFD), conditional functional dependency, functional dependency, RDF data quality, graph-based conditional functional dependencies (GCFD), conditional functional dependency, functional dependency

摘要： 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。

关键词: RDF数据质量, 基于图的条件函数依赖, 条件函数依赖, 函数依赖, RDF数据质量, 基于图的条件函数依赖, 条件函数依赖, 函数依赖