Although the link prediction problem, where missing relation assertions are predicted, has been widely researched, error detection did not receive as much attention. In this paper, we investigate the problem of error detection in relation assertions of knowledge graphs, and we propose an error detection method which relies on path and type features used by a classifier for every relation in the graph exploiting local feature selection. Furthermore, we propose an approach for automatically correcting detected errors originated from confusions between entities. Moreover, we present an approach that translates decision trees trained for relation assertion error detection into SHACL-SPARQL relation constraints. We perform an extensive evaluation on a variety of datasets comparing our error detection approach with state-of-the-art error detection and knowledge completion methods, backed by a manual evaluation on DBpedia and NELL. We evaluate our error correction approach results on DBpedia and NELL and show that the relation constraint induction approach benefits from the higher expressiveness of SHACL and can detect errors which could not be found by automatically learned OWL constraints.

中文翻译：

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关系断言错误的自动检测和关系约束的归纳

尽管对链接缺失问题进行了预测，其中预测了丢失的关系断言，但是对错误检测的关注并不多。在本文中，我们研究了知识图的关系断言中的错误检测问题，并提出了一种错误检测方法，该方法依赖于分类器为图中的每个关系利用分类器使用的路径和类型特征，并利用局部特征选择。此外，我们提出了一种自动纠正由于实体之间的混淆而导致的检测到的错误的方法。此外，我们提出了一种将训练用于关系声明错误检测的决策树转换为SHACL-SPARQL关系约束的方法。我们对错误检测方法与最新的错误检测和知识完成方法进行了广泛的评估，并结合了对DBpedia和NELL的手动评估。我们评估了在DBpedia和NELL上的纠错方法结果，并表明关系约束归纳方法得益于SHACL的更高表达能力，并且可以检测到自动学习的OWL约束找不到的错误。