Knowledge graphs (KGs) have attracted more and more attentions because of their fundamental roles in many tasks. Quality evaluation for KGs is thus crucial and indispensable. Existing methods in this field evaluate KGs by either proposing new quality metrics from different dimensions or measuring performances at KG construction stages. However, there are two major issues with those methods. First, they highly rely on raw data in KGs, which makes KGs' internal information exposed during quality evaluation. Second, they consider more about the quality at data level instead of ability level, where the latter one is more important for downstream applications. To address these issues, we propose a knowledge graph quality evaluation framework under incomplete information (QEII). The quality evaluation task is transformed into an adversarial Q&A game between two KGs. Winner of the game is thus considered to have better qualities. During the evaluation process, no raw data is exposed, which ensures information protection. Experimental results on four pairs of KGs demonstrate that, compared with baselines, the QEII implements a reasonable quality evaluation at ability level under incomplete information.

翻译：知识图谱（KGs）因其在众多任务中的基础性作用而受到越来越多的关注。因此，对KGs进行质量评估至关重要且不可或缺。该领域的现有方法要么通过从不同维度提出新的质量指标，要么通过评估KG构建阶段的表现来评价KGs。然而，这些方法存在两个主要问题。首先，它们高度依赖KGs中的原始数据，这使得KGs的内部信息在质量评估过程中暴露无遗。其次，它们更多考虑的是数据层面的质量，而非能力层面的质量，而后者对于下游应用更为重要。为解决这些问题，我们提出了一种在不完全信息下进行知识图谱质量评估的框架（QEII）。该质量评估任务被转化为两个KGs之间的对抗性问答游戏。因此，游戏的胜者被认为具有更优的质量。在评估过程中，原始数据不会暴露，从而确保了信息保护。在四对KGs上的实验结果表明，与基线方法相比，QEII在不完全信息下实现了能力层面的合理质量评估。