论文标题
知识图质量评估在不完整的信息下
Knowledge Graph Quality Evaluation under Incomplete Information
论文作者
论文摘要
知识图（kg）由于在许多任务中具有基本作用而吸引了越来越多的关注。因此，KGS的质量评估至关重要且必不可少。该领域的现有方法通过提出来自不同维度的新质量指标或在KG施工阶段进行表现来评估KGS。但是，这些方法有两个主要问题。首先，他们高度依赖于公斤的原始数据，这使得在质量评估期间公开了KGS的内部信息。其次，他们更多地考虑了数据级别的质量而不是能力水平，而后者对于下游应用程序更为重要。为了解决这些问题，我们在不完整的信息（QEII）下提出了一个知识图质量评估框架。质量评估任务转变为两个公斤之间的对抗性问答游戏。因此，游戏的获胜者被认为具有更好的品质。在评估过程中，没有暴露原始数据，从而确保信息保护。四对公斤的实验结果表明，与基准相比，QEII在不完整的信息下以能力水平实现了合理的质量评估。
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.
