Grapher: Multi-Stage Knowledge Graph Construction using Pretrained Language Models

Abstract

In this work we address the problem of Knowledge Graph (KG) construction from text, proposing a novel end-to-end multi-stage Grapher system, that separates the overall generation 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 textual descriptions. For each stage we proposed several architectural choices that can be used depending on the available training resources. We evaluated the Grapher on a recent WebNLG 2020 Challenge dataset, achieving competitive results on text-to-RDF generation task, as well as on a recent large-scale TekGen dataset, showing strong overall performance. We believe that the proposed Grapher system can serve as a viable KG construction alternative to the existing linearization or sampling-based graph generation approaches.