论文标题
知识图改进基于三重式伯特网络
Knowledge Graph Refinement based on Triplet BERT-Networks
论文作者
论文摘要
知识图嵌入技术被广泛用于知识图形完善任务，例如图形完成和三重分类。这些技术旨在将知识图（KG）的实体和关系嵌入到低维连续特征空间中。本文采用基于变压器的三重态网络创建了一个嵌入式空间，该空间将有关实体或关系的信息集中在kg中。它从事实和微调创建了文本序列，并通过基于预训练的变压器模型的三重态网络。它遵守依赖有效的空间语义搜索技术的评估范例。我们表明，此评估协议更适合用于关系预测任务的几次设置。我们提出的吉尔伯特方法对三个众所周知的基准知识图（例如FB13，WN11和FB15K）的三重态分类和关系预测任务进行了评估。我们表明，吉尔伯特（Gilbert）在这两个改进任务上取得了更好或可比的结果。
Knowledge graph embedding techniques are widely used for knowledge graph refinement tasks such as graph completion and triple classification. These techniques aim at embedding the entities and relations of a Knowledge Graph (KG) in a low dimensional continuous feature space. This paper adopts a transformer-based triplet network creating an embedding space that clusters the information about an entity or relation in the KG. It creates textual sequences from facts and fine-tunes a triplet network of pre-trained transformer-based language models. It adheres to an evaluation paradigm that relies on an efficient spatial semantic search technique. We show that this evaluation protocol is more adapted to a few-shot setting for the relation prediction task. Our proposed GilBERT method is evaluated on triplet classification and relation prediction tasks on multiple well-known benchmark knowledge graphs such as FB13, WN11, and FB15K. We show that GilBERT achieves better or comparable results to the state-of-the-art performance on these two refinement tasks.
