Knowledge graph (KG) completion aims to find out missing triples in a KG.
Some tasks, such as link prediction and instance completion, have been proposed
for KG completion. They are triple-level tasks with some elements in a missing
triple given to predict the missing element of the triple. However, knowing
some elements of the missing triple in advance is not always a realistic
setting. In this paper, we propose a novel graph-level automatic KG completion
task called Triple Set Prediction (TSP) which assumes none of the elements in
the missing triples is given. TSP is to predict a set of missing triples given
a set of known triples. To properly and accurately evaluate this new task, we
propose 4 evaluation metrics including 3 classification metrics and 1 ranking
metric, considering both the partial-open-world and the closed-world
assumptions. Furthermore, to tackle the huge candidate triples for prediction,
we propose a novel and efficient subgraph-based method GPHT that can predict
the triple set fast. To fairly compare the TSP results, we also propose two
types of methods RuleTensor-TSP and KGE-TSP applying the existing rule- and
embedding-based methods for TSP as baselines. During experiments, we evaluate
the proposed methods on two datasets extracted from Wikidata following the
relation-similarity partial-open-world assumption proposed by us, and also
create a complete family data set to evaluate TSP results following the
closed-world assumption. Results prove that the methods can successfully
generate a set of missing triples and achieve reasonable scores on the new
task, and GPHT performs better than the baselines with significantly shorter
prediction time. The datasets and code for experiments are available at
this https URL

本研究提出了一种新的图级自动知识图谱补全任务，称为三元组集合预测（TSP），该任务旨在预测给定一组已知三元组的一组缺失三元组。为了评估这一新任务，我们提出了 4 个评估指标，包括 3 个分类指标和 1 个排序指标，并考虑了部分开放世界和封闭世界的假设。此外，为了解决庞大的候选三元组预测，我们提出了一种新颖且高效的基于子图的方法 GPHT。在实验证明，该方法可以成功生成一组缺失的三元组，并在新任务上取得了合理的分数，而 GPHT 在预测时间上表现更好，比基线模型显著缩短了预测时间。