Skip to main content
SpringerLink
Log in

A Text Mining Pipeline for Mining the Quantum Cascade Laser Properties

  • Conference paper
  • First Online:
New Trends in Database and Information Systems (ADBIS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1850))

Included in the following conference series:

Abstract

The development of the Terahertz laser technology in quantum cascade lasers (qcl) has brought about great potential for industrial applications. These lasers are based on the Terahertz electromagnetic waves, in the frequency range from about 100 GHz to 10 THz. There is need to understand the structure of the laser and its influence on the performance in order to optimize the design process. One way of collating this information is by having ontologies and knowledge bases capturing the various qcl designs and their performance characteristics. Majority of the laser design data is usually contained in scientific literature. The main drawback of such textual data sources is their unstructured nature. The complex nature of the laser design and the varying author language styles poses some level of difficulty in retrieving this information. Owing to this, the existing methods needs improvement in order retrieve the laser information at a high precision (with minimal number of incorrect records extracted) and minimized number of correct records not extracted. In this paper, we tackle this initial challenge by proposing a text mining pipeline for mining the qcl properties by extending the grammar rules of a conditional random field (CRF) based model using a rule-based approach. The properties of interest include: hetero-structure (laser stacking properties), working temperature, lasing frequency, laser thickness and the optical power. We evaluate the pipeline on sample open access journal papers from AIP, OPTICA and IOP Publishers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kumar, S., Hu, Q., Reno, J.L.: 186 K operation of terahertz quantum-cascade lasers based on a diagonal design. Appl. Phys. Lett. 94(13), 131105 (2009). https://doi.org/10.1063/1.3114418

  2. Vafapour, Z., Keshavarz, A., Ghahraloud, H.: The potential of terahertz sensing for cancer diagnosis. Heliyon 6(12), e05623 (2020). https://doi.org/10.1016/j.heliyon.2020.e05623

    Article  Google Scholar 

  3. Shur, M., Liu, X.: Biomedical applications of terahertz technology. In: Advances in Terahertz Biomedical Imaging and Spectroscopy, vol. 11975, p. 1197502. SPIE, March 2022. https://doi.org/10.1117/12.2604800

  4. Kanno, A., et al.: High-speed coherent transmission using advanced photonics in terahertz bands. IEICE Trans. Electron. 98(12), 1071–1080 (2015). https://doi.org/10.1103/PhysRevMaterials.4.123802

    Article  Google Scholar 

  5. Rosati, E.: The exception for text and data mining (TDM) in the proposed Directive on copyright in the Digital Single Market-technical aspects. Briefing Requested by the Juri Committee, European Parliament (2018). https://doi.org/10.1093/jiplp/jpy063

  6. Liang, H., Stanev, V., Kusne, A.G., Takeuchi, I.: CRYSPNet: crystal structure predictions via neural networks. Phys. Rev. Mater. 4(12), 123802 (2020). https://doi.org/10.1103/PhysRevMaterials.4.123802

    Article  Google Scholar 

  7. Swain, M.C., Cole, J.M.: ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. J. Chem. Inf. Model. 56(10), 1894–1904 (2016). https://doi.org/10.1021/acs.jcim.6b00207

    Article  Google Scholar 

  8. Hawizy, L., Jessop, D.M., Adams, N., Murray-Rust, P.: ChemicalTagger: a tool for semantic text-mining in chemistry. J. Cheminform. 3, 1–13 (2011). https://doi.org/10.1186/1758-2946-3-17

    Article  Google Scholar 

  9. Corbett, P., Copestake, A.: Cascaded classifiers for confidence-based chemical named entity recognition. BMC Bioinform. 9(11), 1–10 (2008). https://doi.org/10.1186/1471-2105-9-S11-S4

    Article  Google Scholar 

  10. García-Remesal, M., García-Ruiz, A., Prez-Rey, D., De La Iglesia, D., Maojo, V.: Using nanoinformatics methods for automatically identifying relevant nanotoxicology entities from the literature. BioMed Res. Int. 2013 (2013). https://doi.org/10.1155/2013/410294

  11. Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)

    Google Scholar 

  12. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  13. Lowe, D.M., Sayle, R.A.: LeadMine: a grammar and dictionary driven approach to entity recognition. J. Cheminform. 7(1), 1–9 (2015). https://doi.org/10.1186/1758-2946-7-S1-S5

    Article  Google Scholar 

  14. Jessop, D.M., Adams, S.E., Willighagen, E.L., Hawizy, L., Murray-Rust, P.: OSCAR4: a flexible architecture for chemical text-mining. J. Cheminform. 3(1), 1–12 (2011). https://doi.org/10.1186/1758-2946-3-41

    Article  Google Scholar 

  15. Rocktäschel, T., Weidlich, M., Leser, U.: ChemSpot: a hybrid system for chemical named entity recognition. Bioinformatics 28(12), 1633–1640 (2012). https://doi.org/10.1093/bioinformatics/bts183

    Article  Google Scholar 

  16. Leaman, R., Wei, C.H., Lu, Z.: tmChem: a high performance approach for chemical named entity recognition and normalization. J. Cheminform. 7(1), 1–10 (2015). https://doi.org/10.1186/1758-2946-7-S1-S3

    Article  Google Scholar 

  17. Dong, Q., Cole, J.M.: Auto-generated database of semiconductor band gaps using chemdataextractor. Sci. Data 9(1), 193 (2022). https://doi.org/10.1038/s41597-022-01294-6

    Article  Google Scholar 

  18. Sierepeklis, O., Cole, J.M.: A thermoelectric materials database auto-generated from the scientific literature using ChemDataExtractor. Sci. Data 9(1), 648 (2022). https://doi.org/10.1038/s41597-022-01752-1

    Article  Google Scholar 

  19. Huang, S., Cole, J.M.: A database of battery materials auto-generated using ChemDataExtractor. Sci. Data 7(1), 260 (2020). https://doi.org/10.1038/s41597-020-00602-2

    Article  Google Scholar 

  20. Zhao, J., Cole, J.M.: A database of refractive indices and dielectric constants auto-generated using chemdataextractor. Sci. Data 9(1), 192 (2022). https://doi.org/10.1038/s41597-022-01295-5

    Article  Google Scholar 

  21. Court, C.J., Cole, J.M.: Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction. Sci. Data 5(1), 1–12 (2018). https://doi.org/10.1038/sdata.2018.111

    Article  Google Scholar 

  22. Mavracic, J., Court, C.J., Isazawa, T., Elliott, S.R., Cole, J.M.: ChemDataExtractor 2.0: autopopulated ontologies for materials science. J. Chem. Inf. Model. 61(9), 4280–4289 (2021). https://doi.org/10.1021/acs.jcim.1c00446

    Article  Google Scholar 

  23. He, T., et al.: Similarity of precursors in solid-state synthesis as text-mined from scientific literature. Chem. Mater. 32(18), 7861–7873 (2020). https://doi.org/10.1021/acs.chemmater.0c02553

    Article  Google Scholar 

  24. Weston, L., et al.: Named entity recognition and normalization applied to large-scale information extraction from the materials science literature. J. Chem. Inf. Model. 59(9), 3692–3702 (2019). https://doi.org/10.1021/acs.jcim.9b00470

    Article  Google Scholar 

  25. Korvigo, I., Holmatov, M., Zaikovskii, A., Skoblov, M.: Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules. J. Chem. 10(1), 1–10 (2018). https://doi.org/10.1186/s13321-018-0280-0

    Article  Google Scholar 

  26. Kononova, O., et al.: Text-mined dataset of inorganic materials synthesis recipes. Sci. Data 6(1), 203 (2019). https://doi.org/10.1038/s41597-019-0224-1

    Article  Google Scholar 

  27. Cruse, K., et al.: Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities. Sci. Data 9(1), 234 (2022). https://doi.org/10.1038/s41597-022-01321-6

    Article  Google Scholar 

  28. Huang, S., Cole, J.M.: BatteryBERT: a pretrained language model for battery database enhancement. J. Chem. Inf. Model. 62(24), 6365–6377 (2022). https://doi.org/10.1021/acs.jcim.2c00035

    Article  Google Scholar 

  29. Zhao, J., Huang, S., Cole, J.M.: OpticalBERT and OpticalTable-SQA: text-and table-based language models for the optical-materials domain. J. Chem. Inf. Model. (2023). https://doi.org/10.1021/acs.jcim.2c01259

  30. Milosevic, N., Gregson, C., Hernandez, R., Nenadic, G.: A framework for information extraction from tables in biomedical literature. Int. J. Doc. Anal. Recognit. (IJDAR) 22, 55–78 (2019). https://doi.org/10.1007/s10032-019-00317-0

    Article  Google Scholar 

  31. Mukaddem, K.T., Beard, E.J., Yildirim, B., Cole, J.M.: ImageDataExtractor: a tool to extract and quantify data from microscopy images. J. Chem. Inf. Model. 60(5), 2492–2509 (2019). https://doi.org/10.1021/acs.jcim.9b00734

    Article  Google Scholar 

  32. Kim, H., Han, J., Han, T.Y.J.: Machine vision-driven automatic recognition of particle size and morphology in SEM images. Nanoscale 12(37), 19461–19469 (2020). https://doi.org/10.1039/D0NR04140H

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the CNRS (French Centre National de la Recherche Scientifique) through the founding of a project within the Programme “Dispositif de Soutien aux Collaborations avec l’Afrique sub-saharienne”. The authors would also like to thank the Strathmore University, School of Computing and Engineering Sciences and the Strathmore University Doctoral Academy for their involvement in creating the opportunity for this work to be produced and lastly, Qingyang Dong (University of Cambridge, Cavendish laboratory-molecular engineering group) for the insightful discussions.

Author information

Authors and Affiliations

  1. SCES, Strathmore University, Nairobi, Kenya

    Deperias Kerre & Dickson Owuor

  2. LIRMM, Univ Montpellier, CNRS, Montpellier, France

    Deperias Kerre & Anne Laurent

  3. IES, Univ Montpellier, CNRS, Montpellier, France

    Kenneth Maussang

Authors
  1. Deperias Kerre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anne Laurent
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenneth Maussang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dickson Owuor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deperias Kerre .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Abelló

  2. University of Ioannina, Ioannina, Greece

    Panos Vassiliadis

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Oscar Romero

  4. Poznań University of Technology, Poznan, Poland

    Robert Wrembel

  5. University of Paris-Saclay, Gif-sur-Yvette, France

    Francesca Bugiotti

  6. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Johann Gamper

  7. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas Solar

  8. University of Calabria, Rende, Italy

    Ester Zumpano

Ethics declarations

Availability of Materials

The source code and the materials used for the production of this work are publicly available at our GitHub repository: https://github.com/DeperiasKerre/qclProperties.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kerre, D., Laurent, A., Maussang, K., Owuor, D. (2023). A Text Mining Pipeline for Mining the Quantum Cascade Laser Properties. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42941-5_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42940-8

  • Online ISBN: 978-3-031-42941-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation