Progressive Knowledge Graph Completion
Jiayi Li Ruilin Luo Jiaqi Sun Jing Xiao Yujiu Yang
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.