Abstract
Knowledge Graphs have been widely used to represent facts in a structured format. Due to their large scale applications, knowledge graphs suffer from being incomplete. The relation prediction task obtains knowledge graph completion by assigning one or more possible relations to each pair of nodes. In this work, we make use of the knowledge graph node names to fine-tune a large language model for the relation prediction task. By utilizing the node names only we enable our model to operate sufficiently in the inductive settings. Our experiments show that we accomplish new scores on a widely used knowledge graph benchmark.
Abstract (translated)
知识图谱已被广泛用于以结构化格式表示事实。由于其大规模应用，知识图谱存在不完整性。关系预测任务通过为每对节点分配一个或多个可能的关系来获得知识图的完成。在这项工作中，我们利用知识图节点名称来微调一个大型语言模型，用于关系预测任务。通过仅使用节点名称，使我们模型能够在归纳设置中充分操作。我们的实验结果表明，我们在广泛使用的知识图基准上实现了新的分数。
URL
https://arxiv.org/abs/2405.02738
