Skip to main content

Predictive Modeling

  • 367 Accesses

Abstract

This chapter covers a comprehensive theoretical framework for predictive modeling (or supervised machine learning). It also covers various biases, challenges, solutions, and use cases of predictive modeling in libraries. A case study that shows how library professionals can use predictive modeling to index/tag future textual resources without repeating a text mining technique, again and again, is also included.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-85085-2_8
  • Chapter length: 30 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-85085-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cordell R (2020) Machine learning + libraries: A report on the state of the field. Library of Congress. https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf. Accessed 18 Dec 2020

  2. Miller J (2020) The new library user: Machine learning. EDUCAUSE Review 55(1). https://er.educause.edu/articles/2020/2/the-new-library-user-machine-learning. Accessed 27 Dec 2020

  3. Shmueli G (2010) To explain or to predict? Statistical Science 25:289–310. https://doi.org/10.1214/10-STS330

    MathSciNet  CrossRef  Google Scholar 

  4. Lamba M, Madhusudhan M (2020) Mapping of ETDs in ProQuest dissertations and theses (PQDT) Global database (2014–2018). Cadernos BAD 2019:169–182. https://www.bad.pt/publicacoes/index.php/cadernos/article/view/2034/pdf

    Google Scholar 

  5. Lamba M, Madhusudhan M (2018) Metadata tagging of library and information science theses: Shodhganga (2013–2017). In: ETD 2018 Taiwan beyond the boundaries of rims and oceans: Globalizing knowledge with ETDs, Taipei, Taiwan. https://zenodo.org/record/1475795#.X9zRTdgzYuE

  6. Lamba M (2019) Text analysis of ETDs in ProQuest dissertations and theses (PQDT) global (2016–2018). In: ICDL2019 digital transformation for an agile environment, New Delhi, India, pp 734–745. https://zenodo.org/record/3545907#.X9zRJtgzYuF

  7. Lamba M, Madhusudhan M (2019) Metadata tagging and prediction modeling: Case study of DESIDOC. J Libr Inf Technol (2008–2017) World Digital Libr 12(1):33–89. https://doi.org/10.18329/09757597/2019/12103

  8. Mahdi AE, Joorabchi A (2011) Automatic subject classification of scientific literature using citation metadata. In: Ariwa E, El-Qawasmeh E (eds) Digital enterprise and information systems. Springer, Berlin, Heidelberg, pp 545–559

    CrossRef  Google Scholar 

  9. Kossmeier M, Heinze G (2019) Predicting future citation counts of scientific manuscripts submitted for publication: A cohort study in transplantology. Transplant International 32:6–15. https://doi.org/10.1111/tri.13292

  10. Momtazi S (2018) Unsupervised latent dirichlet allocation for supervised question classification. Inf Process Manag 54:380–393. https://doi.org/10.1016/j.ipm.2018.01.001

    CrossRef  Google Scholar 

  11. Chen L-C (2017) An effective LDA-based time topic model to improve blog search performance. Inf Process Manag 53:1299–1319. https://doi.org/10.1016/j.ipm.2017.08.001

    CrossRef  Google Scholar 

  12. Hasanain M, Elsayed T (2017) Query performance prediction for microblog search. Inf Process Manag 53:1320–1341. https://doi.org/10.1016/j.ipm.2017.08.002

    CrossRef  Google Scholar 

  13. Nelson LK, Burk D, Knudsen M, McCall L (2018) The future of coding: A comparison of hand-coding and three types of computer-assisted text analysis methods. Sociological Methods Res 50(1):3–44. https://doi.org/10.1177/0049124118769114

    MathSciNet  CrossRef  Google Scholar 

  14. Wagstaff KL, Liu GZ (2018) Automated classification to improve the efficiency of weeding library collections. J Acad Librarianship 44:238–247. https://doi.org/10.1016/j.acalib.2018.02.001

    CrossRef  Google Scholar 

  15. Mrowinski MJ, Fronczak P, Fronczak A, Ausloos M, Nedic O (2017) Artificial intelligence in peer review: How can evolutionary computation support journal editors? PLOS ONE 12:e0184711. https://doi.org/10.1371/journal.pone.0184711

    CrossRef  Google Scholar 

  16. Padilla T (2019) Responsible operations: Data science, machine learning, and AI in libraries. OCLC RESEARCH POSITION PAPER, Dublin, Ohio: OCLC Research. https://doi.org/10.25333/xk7z-9g97. Accessed 18 Dec 2020

    CrossRef  Google Scholar 

  17. Lorang E, Soh L-K, Liu Y, Pack C (2020) Digital libraries, intelligent data analytics, and augmented description: A demonstration project. Library of Congress. https://digitalcommons.unl.edu/libraryscience/396/. Accessed 21 Dec 2020

  18. Murphy O, Villaespesa E (2020) AI: A museum planning toolkit. Goldsmiths, University of London. https://themuseumsainetwork.files.wordpress.com/2020/02/20190317_museums-and-ai-toolkit_rl_web.pdf. Accessed 21 Dec 2020

    Google Scholar 

  19. Fjeld J, Achten N, Hilligoss H, Nagy A, Srikumar M (2020) Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. SSRN Electron J. https://doi.org/10.2139/ssrn.3518482

  20. Fagan B (2016) Chronicling white America. American Periodicals 26(1):10–13. https://dx.doi.org/10.17613/M6VD5X. Accessed 21 Dec 2020

    Google Scholar 

  21. Earl C, White A, Trizna M, Frandsen P, Kawahara A, Brady S, Dikow R (2019) Discovering patterns of biodiversity in insects using deep machine learning. Biodiversity Inf Sci Standards 3:e37525. https://doi.org/10.3897/biss.3.37525

    CrossRef  Google Scholar 

  22. Chen H, Smith TR, Larsgaard ML, Hill LL, Ramsey M (1997) A geographic knowledge representation system for multimedia geospatial retrieval and analysis. Int J Digit Libr 1:132–152. https://doi.org/10.1007/s007990050010

    CrossRef  Google Scholar 

  23. Leung S, Baildon M, Albaugh N (2019) Applying concepts of algorithmic justice to reference, instruction, and collections work. https://dspace.mit.edu/handle/1721.1/122343. Accessed 18 Dec 2020

  24. Brygfjeld SA, Wetjen F, Walsøe (2018) A machine learning for production of dewey decimal. In: World library and information congress: 84th IFLA general conference and assembly, Kuala Lumpur, Malaysia. https://library.ifla.org/2216/1/115-brygfjeld-en.pdf. Accessed 21 Dec 2020

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Lamba, M., Madhusudhan, M. (2022). Predictive Modeling. In: Text Mining for Information Professionals. Springer, Cham. https://doi.org/10.1007/978-3-030-85085-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85085-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85084-5

  • Online ISBN: 978-3-030-85085-2

  • eBook Packages: Computer ScienceComputer Science (R0)