Skip to main content

An Ontology-Enabled Natural Language Processing Pipeline for Provenance Metadata Extraction from Biomedical Text (Short Paper)

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 10033)


Extraction of structured information from biomedical literature is a complex and challenging problem due to the complexity of biomedical domain and lack of appropriate natural language processing (NLP) techniques. High quality domain ontologies model both data and metadata information at a fine level of granularity, which can be effectively used to accurately extract structured information from biomedical text. Extraction of provenance metadata, which describes the history or source of information, from published articles is an important task to support scientific reproducibility. Reproducibility of results reported by previous research studies is a foundational component of scientific advancement. This is highlighted by the recent initiative by the US National Institutes of Health called “Principles of Rigor and Reproducibility”. In this paper, we describe an effective approach to extract provenance metadata from published biomedical research literature using an ontology-enabled NLP platform as part of the Provenance for Clinical and Healthcare Research (ProvCaRe). The ProvCaRe-NLP tool extends the clinical Text Analysis and Knowledge Extraction System (cTAKES) platform using both provenance and biomedical domain ontologies. We demonstrate the effectiveness of ProvCaRe-NLP tool using a corpus of 20 peer-reviewed publications. The results of our evaluation demonstrate that the ProvCaRe-NLP tool has significantly higher recall in extracting provenance metadata as compared to existing NLP pipelines such as MetaMap.


  • Ontology-based natural language processing
  • Provenance metadata
  • Scientific reproducibility
  • Named entity recognition

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

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Learn about institutional subscriptions


  1. 1.

    Courier New font is used to represent ontology classes. The provcare namespace refers to The ProvCaRe ontology is available at:


  1. Sahoo, S.S., Valdez, J., Rueschman, M.: Scientific reproducibility in biomedical research: provenance metadata ontology for semantic annotation of study description. In: American Medical Informatics Association (AMIA) Annual Symposium, Chicago (2016)

    Google Scholar 

  2. Collins, F.S., Tabak, L.A.: Policy: NIH plans to enhance reproducibility. Nature 505, 612–613 (2014)

    CrossRef  Google Scholar 

  3. Landis, S.C., Amara, S.G., Asadullah, K., Austin, C.P., Blumenstein, R., Bradley, E.W., Crystal, R.G., Darnell, R.B., Ferrante, R.J., Fillit, H., Finkelstein, R., Fisher, M., Gendelman, H.E., Golub, R.M., Goudreau, J.L., Gross, R.A., Gubitz, A.K., Hesterlee, S.E., Howells, D.W., Huguenard, J., Kelner, K., Koroshetz, W., Krainc, D., Lazic, S.E., Levine, M.S., Macleod, M.R., McCall, J.M., Moxley III, R.T., Narasimhan, K., Noble, L.J., Perrin, S., Porter, J.D., Steward, O., Unger, E., Utz, U., Silberberg, S.D.: A call for transparent reporting to optimize the predictive value of preclinical research. Nature 490, 187–191 (2012)

    CrossRef  Google Scholar 

  4. Dean, D.A., Goldberger, A.L., Mueller, R., Kim, M., Rueschman, M., Mobley, D., Sahoo, S.S., Jayapandian, C.P., Cui, L., Morrical, M.G., Surovec, S., Zhang, G.Q., Redline, S.: Scaling up scientific discovery in sleep medicine: the National Sleep Research Resource. SLEEP 39, 1151–1164 (2016)

    Google Scholar 

  5. Meystre, S., Savova, G., Kipper-Schuler, K., Hurdle, J.F.: Extracting information from textual documents in the electronic health record: a review of recent research. IMIA Year Book of Med. Inf. 47, 128–144 (2008)

    Google Scholar 

  6. Crowley, R.S., Castine, M., Mitchell, K.J., Chavan, G., McSherry, T., Feldman, M.: caTIES—a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research. J. Am. Med. Inform. Assoc. 17, 253–264 (2010)

    CrossRef  Google Scholar 

  7. Friedman, C.: A broad coverage natural language processing system. In: AMIA Fall Symposium, pp. 270–274 (2000)

    Google Scholar 

  8. Jain, N.L., Knirsch, C.A., Friedman, C., Hripcsak, G.: Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports. In: AMIA Fall Symposium, Philadelphia, pp. 542–546 (1996)

    Google Scholar 

  9. Sneiderman, C.A., Rindflesch, T.C., Bean, C.A.: Identification of anatomical terminology in medical text. In: AMIA Fall Symposium, pp. 428–432 (1998)

    Google Scholar 

  10. Aronson, A.R., Lang, F.M.: An overview of MetaMap: historical perspective and recent advances. J. Am. Med. Inf. Assoc. 17, 229–236 (2010)

    CrossRef  Google Scholar 

  11. Aronson, A.R.: MetaMap: Mapping Text to the UMLS Metathesaurus, US NLM 2006 (2006)

    Google Scholar 

  12. Bodenreider, O.: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, 267–270 (2004)

    CrossRef  Google Scholar 

  13. Jonquet, C., Shah, N.M., Musen, M.A.: The open biomedical annotator. Presented at the AMIA Summit on Translat Bioinformatics, San Francisco (2009)

    Google Scholar 

  14. Savova, G.K., Masanz, J.J., Ogren, P.V., Zheng, J., Sohn, S., Kipper-Schuler, K.C., Chute, C.G.: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17, 507–513 (2010)

    CrossRef  Google Scholar 

  15. Ferrucci, D., Lally, A.: UIMA: an architectural approach to unstructured information processing in the corporate research environment. Nat. Lang. Eng. 10, 327–348 (2004)

    CrossRef  Google Scholar 

  16. OpenNLP.

  17. Gottlieb, D.J., Punjabi, N.M., Mehra, R., Patel, S.R., Quan, S.F., Babineau, D.C., Tracy, R.P., Rueschman, M., Blumenthal, R.S., Lewis, E.F., Bhatt, D.L., Redline, S.: CPAP versus oxygen in obstructive sleep apnea. New England J. Med. 370, 2276–2285 (2014)

    CrossRef  Google Scholar 

  18. Moreau, L., Missier, P.: PROV Data Model (PROV-DM), World Wide Web Consortium W3C 2013 (2013)

    Google Scholar 

Download references


This work is supported in part by the NIH-NIBIB Big Data to Knowledge (BD2 K) 1U01EB020955 and NIH-NHLBI R24 HL114473 grants.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Satya S. Sahoo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Valdez, J., Rueschman, M., Kim, M., Redline, S., Sahoo, S.S. (2016). An Ontology-Enabled Natural Language Processing Pipeline for Provenance Metadata Extraction from Biomedical Text (Short Paper). In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48471-6

  • Online ISBN: 978-3-319-48472-3

  • eBook Packages: Computer ScienceComputer Science (R0)