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OTM Confederated International Conferences "On the Move to Meaningful Internet Systems"

OTM 2016: On the Move to Meaningful Internet Systems: OTM 2016 Conferences pp 699–708Cite as

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)

Abstract

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.

Keywords

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

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Fig. 1.

Notes

  1. 1.

    Courier New font is used to represent ontology classes. The provcare namespace refers to http://www.case.edu/ProvCaRe/provcare. The ProvCaRe ontology is available at: https://sites.google.com/a/case.edu/bmhinformaticsgroup/research/provcare/.

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Acknowledgement

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

  1. Division of Medical Informatics and Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA

    Joshua Valdez & Satya S. Sahoo

  2. Departments of Medicine, Brigham and Women’s Hospital and Beth Israel Deaconess Medical Center, Harvard University, Boston, MA, USA

    Michael Rueschman, Matthew Kim & Susan Redline

Authors
  1. Joshua Valdez
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  2. Michael Rueschman
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  3. Matthew Kim
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  4. Susan Redline
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  5. Satya S. Sahoo
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Corresponding author

Correspondence to Satya S. Sahoo .

Editor information

Editors and Affiliations

  1. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Christophe Debruyne

  2. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  3. TU Graz, Graz, Austria

    Robert Meersman

  4. La Trobe University, Melbourne, Australia

    Tharam Dillon

  5. Institute of Computer Languages, TU Wien, Vienna, Austria

    eva Kühn

  6. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Declan O'Sullivan

  7. Università degli Studi di Milano Crema, Crema, Italy

    Claudio Agostino Ardagna

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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: , et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_43

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  • DOI: https://doi.org/10.1007/978-3-319-48472-3_43

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