Self-supervised knowledge-graph completion (KGC) relies on estimating a
scoring model over (entity, relation, entity)-tuples, for example, by embedding
an initial knowledge graph. Prediction quality can be improved by calibrating
the scoring model, typically by adjusting the prediction thresholds using
manually annotated examples. In this paper, we attempt for the first time
cold-start calibration for KGC, where no annotated examples exist initially for
calibration, and only a limited number of tuples can be selected for
annotation. Our new method ACTC finds good per-relation thresholds efficiently
based on a limited set of annotated tuples. Additionally to a few annotated
tuples, ACTC also leverages unlabeled tuples by estimating their correctness
with Logistic Regression or Gaussian Process classifiers. We also experiment
with different methods for selecting candidate tuples for annotation:
density-based and random selection. Experiments with five scoring models and an
oracle annotator show an improvement of 7% points when using ACTC in the
challenging setting with an annotation budget of only 10 tuples, and an average
improvement of 4% points over different budgets.

本文首次尝试对无标注实体关系三元组进行冷启动校准，通过 ACTC 方法在受限的标注实体关系三元组集上高效地找到适合的逐关系阈值；此外，ACTC 还利用 logistic 回归或高斯过程分类器估计未标注实体关系三元组的正确性。本文实验表明，在标注实体关系三元组数量有限的情况下，使用 ACTC 可使评分模型在知识图谱补全任务中的预测质量提高。