论文标题
知识图上的归纳链路预测的开放挑战
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
论文作者
论文摘要
表示对知识图（kgs）的表示趋势的新兴趋势超出了固定的已知实体集体链接预测任务，而有利于归纳任务，这意味着在一个图上进行训练，并对没有看到的实体对新图进行推断。在归纳设置中，节点特征通常不可用，并且训练浅实体嵌入矩阵是毫无意义的，因为它们在推理时不能与看不见的实体一起使用。尽管兴趣越来越大，但没有足够的基准来评估归纳代表学习方法。在这项工作中，我们介绍了ILPC 2022，这是KG归纳链路预测的一个新颖的开放挑战。为此，我们构建了两个基于Wikidata的新数据集，这些数据集具有各种尺寸的培训和推理图，这些训练和推理图比现有的电感基准要大得多。我们还提供了两个强大的基准，以利用最近提出的电感方法。我们希望这一挑战有助于简化归纳图表示学习领域的社区努力。 ILPC 2022遵循评估公平性和可重复性的最佳实践，可在https://github.com/pykeen/ilpc2022上获得。
An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities. In inductive setups, node features are often not available and training shallow entity embedding matrices is meaningless as they cannot be used at inference time with unseen entities. Despite the growing interest, there are not enough benchmarks for evaluating inductive representation learning methods. In this work, we introduce ILPC 2022, a novel open challenge on KG inductive link prediction. To this end, we constructed two new datasets based on Wikidata with various sizes of training and inference graphs that are much larger than existing inductive benchmarks. We also provide two strong baselines leveraging recently proposed inductive methods. We hope this challenge helps to streamline community efforts in the inductive graph representation learning area. ILPC 2022 follows best practices on evaluation fairness and reproducibility, and is available at https://github.com/pykeen/ilpc2022.
