Recent success of Knowledge Graph has spurred widespread interests in methods for the problem of Knowledge Graph completion. However, efforts to understand the quality of the candidate triples from these methods, in particular from the schema aspect, have been limited. Indeed, most existing Knowledge Graph completion methods do not guarantee that the expanded Knowledge Graphs are consistent with the ontological schema of the initial Knowledge Graph. In this work, we challenge the silver standard method, by proposing the notion of schema-correctness. A fundamental challenge is how to make use of different types of Knowledge Graph completion methods together to improve the production of schema-correct triples. To address this, we analyse the characteristics of different methods and propose a schema aware iterative approach to Knowledge Graph completion. Our main findings are: (i) Some popular Knowledge Graph completion methods have surprisingly low schema-correctness ratio; (ii) Different types of Knowledge Graph completion methods can work with each other to help overcame individual limitations; (iii) Some iterative sequential combinations of Knowledge Graph completion methods have significantly better schema-correctness and coverage ratios than other combinations; (iv) All the MapReduce based iterative methods outperform involved single-pass methods significantly over the tested Knowledge Graphs in terms of productivity of schema-correct triples. Our findings and infrastructure can help further work on evaluating Knowledge Graph completion methods, more fine-grained approaches for schema aware iterative knowledge graph completion, as well as new approximate reasoning approaches based Knowledge Graph completion methods.

知识图的最新成功激发了人们对解决知识图完成问题的方法的广泛兴趣。但是，从这些方法（尤其是从架构方面）了解候选三元组质量的工作受到了限制。确实，大多数现有的知识图完成方法不能保证扩展的知识图与初始知识图的本体架构是一致的。在这项工作中，我们通过提出架构正确性的概念来挑战银标准方法。一个基本的挑战是如何一起使用不同类型的知识图完成方法来改进模式正确的三元组的生成。为了解决这个问题，我们分析了不同方法的特征，并提出了一种模式感知的迭代方法来完成知识图。我们的主要发现是：（i）一些流行的知识图完成方法具有令人惊讶的低方案正确率；（ii）不同类型的知识图完成方法可以相互配合，以帮助克服个人的局限性；（iii）某些知识图完成方法的迭代顺序组合比其他组合具有更好的方案正确性和覆盖率；（iv）就模式正确的三元组的生产率而言，所有基于MapReduce的迭代方法的性能都优于测试的知识图所涉及的单遍方法。我们的发现和基础架构可以帮助您进一步评估知识图完成方法，