mlsanityData to 14 Oct 2026

SynKB: Semantic Search for Synthetic Procedures

arXiv 2208.07400 ↗First posted 15 Aug 2022cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

9 of 23 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
  3. 2019The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structurescited by 49
  4. 2021Pre-train or Annotate? Domain Adaptation with a Constrained Budgetcited by 27
  5. 2018An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocolscited by 18
  6. 2021Process-Level Representation of Scientific Protocols with Interactive Annotationcited by 16
  7. 2020Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literaturecited by 15
  8. 2020Extracting a Knowledge Base of COVID-19 Events from Social Mediacited by 7
  9. 2021Neural Extractive Searchcited by 2

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Citing papers per year: 2022: 1; 2023: 3.

4 papers.