Assessing the quality of an evolving knowledge base is a challenging task as it often requires to identify correct quality assessment procedures. Since data is often derived from autonomous, and increasingly large data sources, it is impractical to manually curate the data, and challenging to continuously and automatically assess their quality. In this paper, we explore two main areas of quality assessment related to evolving knowledge bases: (i) identification of completeness issues using knowledge base evolution analysis, and (ii) identification of consistency issues based on integrity constraints, such as minimum and maximum cardinality, and range constraints. For the completeness analysis, we use data profiling information from consecutive knowledge base releases to estimate completeness measures that allow predicting quality issues. Then, we perform consistency checks to validate the results of the completeness analysis using integrity constraints and learning models. The approach has been tested both quantitatively and qualitatively by using a subset of datasets from both DBpedia and 3cixty knowledge bases. The performance of the approach is evaluated using precision, recall, and F1 score. From completeness analysis, we observe a 94% precision for the English DBpedia KB and 95% precision for the 3cixty Nice KB. We also assessed the performance of our consistency analysis by using five learning models over three sub-tasks, namely minimum cardinality, maximum cardinality, and range constraint. We observed that the best performing model in our experimental setup is Random Forest, reaching an F1 score greater than 90% for minimum and maximum cardinality and 84% for range constraints.

评估不断发展的知识库的质量是一项艰巨的任务，因为它通常需要确定正确的质量评估程序。由于数据通常来自自治的且越来越大的数据源，因此手动管理数据是不切实际的，并且连续不断地自动评估其质量是一项挑战。在本文中，我们探索了与不断发展的知识库有关的质量评估的两个主要领域：（i）使用知识库演化分析识别完整性问题，以及（ii）根据完整性约束（例如最小和最大基数）识别一致性问题，以及范围限制。对于完整性分析，我们使用来自连续知识库发布的数据概要分析信息来估计允许预测质量问题的完整性度量。然后，我们使用完整性约束和学习模型执行一致性检查，以验证完整性分析的结果。通过使用来自DBpedia和3cixty知识库的数据集的子集，对该方法进行了定量和定性测试。使用精度，召回率和F1分数评估该方法的性能。通过完整性分析，我们发现英语DBpedia KB的精度为94％，3cixty Nice KB的精度为95％。我们还通过对三个子任务（即最小基数，最大基数和范围约束）使用五个学习模型来评估一致性分析的性能。我们观察到，在我们的实验设置中，性能最佳的模型是随机森林，最小和最大基数的F1得分均大于90％，范围约束的F1得分大于84％。