IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i8d10.1007_s10845-021-01807-x.html
   My bibliography  Save this article

“FabNER”: information extraction from manufacturing process science domain literature using named entity recognition

Author

Listed:
  • Aman Kumar

    (North Carolina State University)

  • Binil Starly

    (North Carolina State University)

Abstract

The number of published manufacturing science digital articles available from scientific journals and the broader web have exponentially increased every year since the 1990s. To assimilate all of this knowledge by a novice engineer or an experienced researcher, requires significant synthesis of the existing knowledge space contained within published material, to find answers to basic and complex queries. Algorithmic approaches through machine learning and specifically Natural Language Processing (NLP) on a domain specific area such as manufacturing, is lacking. One of the significant challenges to analyzing manufacturing vocabulary is the lack of a named entity recognition model that enables algorithms to classify the manufacturing corpus of words under various manufacturing semantic categories. This work presents a supervised machine learning approach to categorize unstructured text from 500K+ manufacturing science related scientific abstracts and labelling them under various manufacturing topic categories. A neural network model using a bidirectional long-short term memory, plus a conditional random field (BiLSTM + CRF) is trained to extract information from manufacturing science abstracts. Our classifier achieves an overall accuracy (f1-score) of 88%, which is quite near to the state-of-the-art performance. Two use case examples are presented that demonstrate the value of the developed NER model as a Technical Language Processing (TLP) workflow on manufacturing science documents. The long term goal is to extract valuable knowledge regarding the connections and relationships between key manufacturing concepts/entities available within millions of manufacturing documents into a structured labeled-property graph data structure that allow for programmatic query and retrieval.

Suggested Citation

  • Aman Kumar & Binil Starly, 2022. "“FabNER”: information extraction from manufacturing process science domain literature using named entity recognition," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2393-2407, December.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01807-x
    DOI: 10.1007/s10845-021-01807-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01807-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01807-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sharma, Amalesh & Adhikary, Anirban & Borah, Sourav Bikash, 2020. "Covid-19′s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data," Journal of Business Research, Elsevier, vol. 117(C), pages 443-449.
    2. Ruben Costa & Celson Lima & João Sarraipa & Ricardo Jardim-Gonçalves, 2016. "Facilitating knowledge sharing and reuse in building and construction domain: an ontology-based approach," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 263-282, February.
    3. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
    4. Michael Gusenbauer, 2019. "Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 177-214, January.
    5. Antonio L. Alfeo & Mario G. C. A. Cimino & Gigliola Vaglini, 2021. "Technological troubleshooting based on sentence embedding with deep transformers," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1699-1710, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jia-Min Lu & Hui-Feng Wang & Qi-Hang Guo & Jian-Wei Wang & Tong-Tong Li & Ke-Xin Chen & Meng-Ting Zhang & Jian-Bo Chen & Qian-Nuan Shi & Yi Huang & Shao-Wen Shi & Guang-Yong Chen & Jian-Zhang Pan & Zh, 2024. "Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Ali Zackery & Joseph Amankwah-Amoah & Zahra Heidari Darani & Shiva Ghasemi, 2022. "COVID-19 Research in Business and Management: A Review and Future Research Agenda," Sustainability, MDPI, vol. 14(16), pages 1-32, August.
    3. Viswanathan Nagarajan & Prateek Sharma, 2021. "Firm internationalization and long‐term impact of the Covid‐19 pandemic," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1477-1491, September.
    4. Ananthan Nambiar & Tobias Rubel & James McCaull & Jon deVries & Mark Bedau, 2021. "Dropping diversity of products of large US firms: Models and measures," Papers 2110.08367, arXiv.org.
    5. Paul, Ananna & Shukla, Nagesh & Trianni, Andrea, 2023. "Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    6. Papanagnou, Christos & Seiler, Andreas & Spanaki, Konstantina & Papadopoulos, Thanos & Bourlakis, Michael, 2022. "Data-driven digital transformation for emergency situations: The case of the UK retail sector," International Journal of Production Economics, Elsevier, vol. 250(C).
    7. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    8. Norma Salgado-Orellana & Emilio Berrocal de-Luna & Calixto Gutiérrez-Braojos, 2021. "A scientometric study of doctoral theses on the Roma in the Iberian Peninsula during the 1977–2018 period," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 437-458, January.
    9. Kofi A. A-O. Agyei-Henaku & Charlotte Badu-Prah & Francis Srofenyoh & Ferguson K. Gidiglo & Akua Agyeiwaa-Afrane & Justice G. Djokoto, 2024. "Citations of Publications on Foreign Direct Investments into Agribusiness: Nature, Variability and Drivers," SAGE Open, , vol. 14(1), pages 21582440241, February.
    10. Johannes Stübinger & Lucas Schneider, 2020. "Understanding Smart City—A Data-Driven Literature Review," Sustainability, MDPI, vol. 12(20), pages 1-23, October.
    11. Simone Belli & Carlos Gonzalo-Penela, 2020. "Science, research, and innovation infospheres in Google results of the Ibero-American countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 635-653, May.
    12. Wu, Lingfei & Kittur, Aniket & Youn, Hyejin & Milojević, Staša & Leahey, Erin & Fiore, Stephen M. & Ahn, Yong-Yeol, 2022. "Metrics and mechanisms: Measuring the unmeasurable in the science of science," Journal of Informetrics, Elsevier, vol. 16(2).
    13. Huimin Liu & Yupeng Shi & Xuze Yang & Wentao Zhang, 2023. "The Role of Business Environment and Digital Government in Mitigating Supply Chain Vulnerability—Evidence from the COVID-19 Shock," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    14. Gordana Ispirova & Tome Eftimov & Barbara Koroušić Seljak, 2020. "P-NUT: Predicting NUTrient Content from Short Text Descriptions," Mathematics, MDPI, vol. 8(10), pages 1-21, October.
    15. Ibrahim Ahmed Ghashim & Muhammad Arshad, 2023. "Internet of Things (IoT)-Based Teaching and Learning: Modern Trends and Open Challenges," Sustainability, MDPI, vol. 15(21), pages 1-21, November.
    16. Katia A. Figueroa-Rodríguez & Francisco Hernández-Rosas & Benjamín Figueroa-Sandoval & Joel Velasco-Velasco & Noé Aguilar Rivera, 2019. "What Has Been the Focus of Sugarcane Research? A Bibliometric Overview," IJERPH, MDPI, vol. 16(18), pages 1-15, September.
    17. Hanna Obracht-Prondzyńska & Ewa Duda & Helena Anacka & Jolanta Kowal, 2022. "Greencoin as an AI-Based Solution Shaping Climate Awareness," IJERPH, MDPI, vol. 19(18), pages 1-25, September.
    18. Enrique Orduña-Malea & Rodrigo Costas, 2021. "Link-based approach to study scientific software usage: the case of VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8153-8186, September.
    19. Borgards, Oliver & Czudaj, Robert L. & Hoang, Thi Hong Van, 2021. "Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact," Resources Policy, Elsevier, vol. 71(C).
    20. Guido A. Veldhuis & Dominique Blok & Maaike H.T. de Boer & Gino J. Kalkman & Roos M. Bakker & Rob P.M. van Waas, 2024. "From text to model: Leveraging natural language processing for system dynamics model development," System Dynamics Review, System Dynamics Society, vol. 40(3), July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01807-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.