论文标题
文本的知识图生成
Knowledge Graph Generation From Text
论文作者
论文摘要
在这项工作中，我们提出了一个新颖的端到端多阶段知识图（kg）生成系统，从文本输入将整个过程分为两个阶段。首先使用验证的语言模型生成图节点，然后使用简单的边缘构造头，从而从文本中提取有效的KG。对于每个阶段，我们考虑几种可以根据可用培训资源使用的建筑选择。我们在最近的WebNLG 2020挑战数据集上评估了该模型，与文本到RDF生成任务的最先进性能以及纽约时报（NYT）和大型Tekgen数据集匹配，显示出强大的整体性能，表现出强大的总体性能，超越了现有基准。我们认为，提出的系统可以作为现有线性化或基于抽样的图生成方法的可行kg构造替代品。我们的代码可以在https://github.com/ibm/grapher上找到
In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages. The graph nodes are generated first using pretrained language model, followed by a simple edge construction head, enabling efficient KG extraction from the text. For each stage we consider several architectural choices that can be used depending on the available training resources. We evaluated the model on a recent WebNLG 2020 Challenge dataset, matching the state-of-the-art performance on text-to-RDF generation task, as well as on New York Times (NYT) and a large-scale TekGen datasets, showing strong overall performance, outperforming the existing baselines. We believe that the proposed system can serve as a viable KG construction alternative to the existing linearization or sampling-based graph generation approaches. Our code can be found at https://github.com/IBM/Grapher
