Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the model from scratch in principle, such an approach is infeasible for large-scale KGs, where retraining is expensive and new entities may arise frequently. In this paper, we propose and describe a large-scale benchmark to evaluate semi-inductive LP models. The benchmark is based on and extends Wikidata5M: It provides transductive, k-shot, and 0-shot LP tasks, each varying the available information from (i) only KG structure, to (ii) including textual mentions, and (iii) detailed descriptions of the entities. We report on a small study of recent approaches and found that semi-inductive LP performance is far from transductive performance on long-tail entities throughout all experiments. The benchmark provides a test bed for further research into integrating context and textual information in semi-inductive LP models.

翻译：半归纳链接预测是知识图谱中一项基于上下文信息预测先前未见新实体事实的任务。尽管原则上可以通过从头重新训练模型来整合新实体，但此类方法对于大规模知识图谱而言不可行，因为重新训练成本高昂且新实体可能频繁出现。本文提出并描述了一个用于评估半归纳链接预测模型的大规模基准测试。该基准测试基于并扩展了Wikidata5M：它提供了直推式、k样本和零样本链接预测任务，每项任务的信息可获取范围从(i)仅知识图谱结构，到(ii)包含文本提及，再到(iii)实体的详细描述。我们针对近期方法进行了一项小型研究，发现所有实验中，半归纳链接预测在长尾实体上的性能均远低于直推式性能。该基准测试为深入研究在半归纳链接预测模型中整合上下文与文本信息提供了实验平台。