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.

翻译：知识图谱（KGs）被广泛应用于人工智能应用，如问答系统和推荐系统。然而，知识图谱常被发现存在不完整的情况。尽管现有文献大多关注于为给定的不完整知识图谱三元组预测缺失节点，但仍有机会通过探索现有节点之间的关系来补全知识图谱，这一任务被称为关系预测。在本研究中，我们提出了一种关系预测模型，该模型利用知识图谱中的文本信息和结构信息。我们的方法将基于路径的嵌入与语言模型嵌入相结合，以有效表示节点。我们证明，该模型在广泛使用的数据集上进行关系预测任务评估时，达到了具有竞争力的结果。