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        <title>Knowledge Graph Generation From Text</title>
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        <namePart type="given">Igor</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.116</identifier>
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%0 Conference Proceedings
%T Knowledge Graph Generation From Text
%A Melnyk, Igor
%A Dognin, Pierre
%A Das, Payel
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F melnyk-etal-2022-knowledge
%X 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.
%R 10.18653/v1/2022.findings-emnlp.116
%U https://aclanthology.org/2022.findings-emnlp.116/
%U https://doi.org/10.18653/v1/2022.findings-emnlp.116
%P 1610-1622

Markdown (Informal)

[Knowledge Graph Generation From Text](https://aclanthology.org/2022.findings-emnlp.116/) (Melnyk et al., Findings 2022)

• Knowledge Graph Generation From Text (Melnyk et al., Findings 2022)

ACL

• Igor Melnyk, Pierre Dognin, and Payel Das. 2022. Knowledge Graph Generation From Text. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1610–1622, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.