@inproceedings{bai-etal-2022-synkb,
title = "{S}yn{KB}: Semantic Search for Synthetic Procedures",
author = "Bai, Fan and
Ritter, Alan and
Madrid, Peter and
Freitag, Dayne and
Niekrasz, John",
editor = "Che, Wanxiang and
Shutova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.31",
doi = "10.18653/v1/2022.emnlp-demos.31",
pages = "311--318",
abstract = "In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures. By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes. Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SynKB has higher recall than Reaxsys, while maintaining high precision. We plan to make SynKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions.",
}
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<abstract>In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures. By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes. Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SynKB has higher recall than Reaxsys, while maintaining high precision. We plan to make SynKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions.</abstract>
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%0 Conference Proceedings
%T SynKB: Semantic Search for Synthetic Procedures
%A Bai, Fan
%A Ritter, Alan
%A Madrid, Peter
%A Freitag, Dayne
%A Niekrasz, John
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F bai-etal-2022-synkb
%X In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols. Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures. By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes. Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU patents, we show that for many queries, SynKB has higher recall than Reaxsys, while maintaining high precision. We plan to make SynKB available as an open-source tool; in contrast, proprietary chemistry databases require costly subscriptions.
%R 10.18653/v1/2022.emnlp-demos.31
%U https://aclanthology.org/2022.emnlp-demos.31
%U https://doi.org/10.18653/v1/2022.emnlp-demos.31
%P 311-318
Markdown (Informal)
[SynKB: Semantic Search for Synthetic Procedures](https://aclanthology.org/2022.emnlp-demos.31) (Bai et al., EMNLP 2022)
ACL
- Fan Bai, Alan Ritter, Peter Madrid, Dayne Freitag, and John Niekrasz. 2022. SynKB: Semantic Search for Synthetic Procedures. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 311–318, Abu Dhabi, UAE. Association for Computational Linguistics.