Knowledge Graph Generation From Text
This work addresses the need for efficient knowledge graph construction from text, offering a viable alternative to existing methods for researchers and practitioners in natural language processing and knowledge representation.
The authors tackled the problem of generating knowledge graphs from text by proposing a novel end-to-end multi-stage system that separates node generation and edge construction, achieving state-of-the-art performance on the WebNLG 2020 Challenge dataset and outperforming baselines on NYT and TekGen datasets.
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