基于概率近似的关联数据集质量评估
Quality Assessment of Linked Datasets using Probabilistic Approximation
摘要：
随着关联开放数据应用的日益广泛，通过计算质量度量来评估数据集的质量成为一个至关重要的问题。对于大型和不断发展的数据集，精确的，确定性的计算质量度量是太耗时或昂贵。我们采用概率技术，如水库采样，布隆过滤器和聚类系数估计，以近似但足够准确的方式实现了广泛的数据质量指标。我们的实现集成在全面的数据质量评估框架Luzzu中。我们在广泛相关的链接开放数据集上评估了其性能和准确性。
英文摘要：
With the increasing application of Linked Open Data, assessing the quality of datasets by computing quality metrics becomes an issue of crucial importance. For large and evolving datasets, an exact, deterministic computation of the quality metrics is too time consuming or expensive. We employ probabilistic techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient estimation for implementing a broad set of data quality metrics in an approximate but sufficiently accurate way. Our implementation is integrated in the comprehensive data quality assessment framework Luzzu. We evaluated its performance and accuracy on Linked Open Datasets of broad relevance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
