  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.
