Knowledge Graph Completion (KGC) has emerged as a promising solution to
address the issue of incompleteness within Knowledge Graphs (KGs). Traditional
KGC research primarily centers on triple classification and link prediction.
Nevertheless, we contend that these tasks do not align well with real-world
scenarios and merely serve as surrogate benchmarks. In this paper, we
investigate three crucial processes relevant to real-world construction
scenarios: (a) the verification process, which arises from the necessity and
limitations of human verifiers; (b) the mining process, which identifies the
most promising candidates for verification; and (c) the training process, which
harnesses verified data for subsequent utilization; in order to achieve a
transition toward more realistic challenges. By integrating these three
processes, we introduce the Progressive Knowledge Graph Completion (PKGC) task,
which simulates the gradual completion of KGs in real-world scenarios.
Furthermore, to expedite PKGC processing, we propose two acceleration modules:
Optimized Top-$k$ algorithm and Semantic Validity Filter. These modules
significantly enhance the efficiency of the mining procedure. Our experiments
demonstrate that performance in link prediction does not accurately reflect
performance in PKGC. A more in-depth analysis reveals the key factors
influencing the results and provides potential directions for future research.

在这篇论文中，我们研究了与真实世界建设场景相关的三个重要过程：验证过程，挖掘过程和训练过程，并通过整合这三个过程引入了渐进式知识图谱完成（PKGC）任务，以模拟在真实世界场景中逐步完善知识图谱的过程。此外，为了加快 PKGC 处理，我们提出了两个加速模块：优化的 Top-k 算法和语义有效性过滤器，这些模块显著提高了挖掘过程的效率。我们的实验证明链接预测的性能不能准确反映 PKGC 的性能，更深入的分析揭示了影响结果的关键因素，并为未来的研究提供了潜在的方向。