Abstract
Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing literature focuses on predicting missing nodes for given incomplete KG triples, there remains an opportunity to complete KGs by exploring relations between existing nodes, a task known as relation prediction. In this study, we propose a relations prediction model that harnesses both textual and structural information within KGs. Our approach integrates walks-based embeddings with language model embeddings to effectively represent nodes. We demonstrate that our model achieves competitive results in the relation prediction task when evaluated on a widely used dataset.
Abstract (translated)
知识图（KGs）广泛应用于人工智能领域，如问答和推荐系统。然而，KGs经常被发现不完整。虽然现有文献主要关注预测给定不完整的KG三元组中的缺失节点，但在关系预测领域仍有机会通过探索现有节点之间的关系来完成KGs，实现名为关系预测的任务。在这项研究中，我们提出了一个利用KGs中文本和结构信息的关联预测模型。我们的方法结合了走行嵌入和语言模型嵌入，有效地表示节点。我们证明了，当在我们的广泛使用数据集上评估时，我们的模型在关系预测任务上实现了具有竞争力的结果。
URL
https://arxiv.org/abs/2404.16206
