Skip to main content
SpringerLink
Log in

An Extensible Ontology Modeling Approach Using Post Coordinated Expressions for Semantic Provenance in Biomedical Research

  • Conference paper
  • First Online:
On the Move to Meaningful Internet Systems. OTM 2017 Conferences (OTM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10574))

Abstract

Provenance metadata describing the source or origin of data is critical to verify and validate results of scientific experiments. Indeed, reproducibility of scientific studies is rapidly gaining significant attention in the research community, for example biomedical and healthcare research. To address this challenge in the biomedical research domain, we have developed the Provenance for Clinical and Healthcare Research (ProvCaRe) using World Wide Web Consortium (W3C) PROV specifications, including the PROV Ontology (PROV-O). In the ProvCaRe project, we are extending PROV-O to create a formal model of provenance information that is necessary for scientific reproducibility and replication in biomedical research. However, there are several challenges associated with the development of the ProvCaRe ontology, including: (1) Ontology engineering: modeling all biomedical provenance-related terms in an ontology has undefined scope and is not feasible before the release of the ontology; (2) Redundancy: there are a large number of existing biomedical ontologies that already model relevant biomedical terms; and (3) Ontology maintenance: adding or deleting terms from a large ontology is error prone and it will be difficult to maintain the ontology over time. Therefore, in contrast to modeling all classes and properties in an ontology before deployment (also called precoordination), we propose the “ProvCaRe Compositional Grammar Syntax” to model ontology classes on-demand (also called postcoordination). The compositional grammar syntax allows us to re-use existing biomedical ontology classes and compose provenance-specific terms that extend PROV-O classes and properties. We demonstrate the application of this approach in the ProvCaRe ontology and the use of the ontology in the development of the ProvCaRe knowledgebase that consists of more than 38 million provenance triples automatically extracted from 384,802 published research articles using a text processing workflow.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    prov represents the http://www.w3.org/ns/prov#namespace.

  2. 2.

    The namespace for the terms used in the expressions are not repeated for brevity.

References

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

    Article  Google Scholar 

  2. Landis, S.C., Amara, S.G., Asadullah, K., et al.: A call for transparent reporting to optimize the predictive value of preclinical research. Nature 490(7419), 187–191 (2012)

    Article  Google Scholar 

  3. Redline, S., Dean III, D., Sanders, M.H.: Entering the era of “Big Data”: getting our metrics right. SLEEP 36(4), 465–469 (2013)

    Article  Google Scholar 

  4. Baker, M.: 1,500 scientists lift the lid on reproducibility. Nature 533(7604), 452–454 (2016)

    Article  Google Scholar 

  5. NIH: Principles and Guidelines for Reporting Preclinical Research (2016). https://www.nih.gov/research-training/rigor-reproducibility/principles-guidelines-reporting-preclinical-research. Accessed 20 July 2017

  6. Buneman, P., Davidson, S.: Data provenance - the foundation of data quality (2010)

    Google Scholar 

  7. Goble, C.: Position statement: musings on provenance, workflow and (semantic web) annotations for bioinformatics. In: Workshop on Data Derivation and Provenance, Chicago (2002)

    Google Scholar 

  8. Sahoo, S.S., Sheth, A., Henson, C.: Semantic provenance for escience: managing the deluge of scientific data. IEEE Internet Comput. 12(4), 46–54 (2008)

    Article  Google Scholar 

  9. Valdez, J., Kim, M., Rueschman, M., Socrates, V., Redline, S., Sahoo, S.S.: ProvCaRe semantic provenance knowledgebase: evaluating scientific reproducibility of research studies. Presented at the American Medical Informatics Association (AMIA) Annual Conference, Washington DC (2017)

    Google Scholar 

  10. Zhao, J., Goble, C., Stevens, R., Turi, D.: Mining Taverna’s semantic web of provenance. J. Concurr. Comput. Practice Exp. 20(5), 463–472 (2008)

    Article  Google Scholar 

  11. Simmhan, Y.L., Plale, A.B., Gannon, A.D.: A survey of data provenance in e-science. SIGMOD Rec. 34(3), 31–36 (2005)

    Article  Google Scholar 

  12. Moreau, L., Clifford, B., Freire, J., et al.: The open provenance model core specification (v1.1). Future Gener. Comput. Syst. 27(6), 743–756 (2010)

    Google Scholar 

  13. Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S.: OWL 2 Web Ontology Language Primer. In: W3C Recommendation. World Wide Web Consortium W3C (2009)

    Google Scholar 

  14. Sahoo, S.S., Sheth, A.: Provenir ontology: towards a framework for eScience provenance management. Presented at the Microsoft eScience Workshop, Pittsburgh, USA, October 2009

    Google Scholar 

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

    Google Scholar 

  16. Lebo, T., Sahoo, S.S., McGuinness, D.; PROV-O: the PROV ontology. In: W3C Recommendation. World Wide Web Consortium W3C (2013)

    Google Scholar 

  17. Cheney, J., Missier, P., Moreau, L.: Constraints of the PROV data model. In: W3C Recommendation. World Wide Web Consortium W3C (2013)

    Google Scholar 

  18. Dean, D.A., Goldberger, A.L., Mueller, R., Kim, M., et al.: Scaling up scientific discovery in sleep medicine: the National Sleep Research Resource. SLEEP 39(5), 1151–1164 (2016)

    Article  Google Scholar 

  19. Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 concepts and abstract syntax. In: W3C Recommendation, World Wide Web Consortium (W3C) (2014)

    Google Scholar 

  20. Rector, A., Luigi, I.: Lexically suggest, logically define: quality assurance of the use of qualifiers and expected results of post-coordination in SNOMED CT. J. Biomed. Inform. 45(2), 199–209 (2012)

    Article  Google Scholar 

  21. Musen, M.A., Noy, N.F., Shah, N.H., Whetzel, P.L., Chute, C.G., Story, M.A., Smith, B.: NCBO team: The national center for biomedical ontology. J. Am. Med. Inform. Assoc. 19(2), 190–195 (2012)

    Article  Google Scholar 

  22. Köhler, S., Doelken, S.C., Mungall, C.J., et al.: The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, 966–974 (2014). Database Issue

    Article  Google Scholar 

  23. Giannangelo, K., Fenton, S.: SNOMED CT survey: an assessment of implementation in EMR/EHR applications. Perspect Health Inf. Manag. 5, 7 (2008)

    Google Scholar 

  24. Bodenreider, O., Stevens, R.: Bio-ontologies: current trends and future directions. Brief. Bioinform. 7(3), 256–274 (2006)

    Article  Google Scholar 

  25. Sim, I., Tu, S.W., Carini, S., Lehmann, H.P., Pollock, B.H., Peleg, M., Wittkowski, K.M.: The ontology of clinical research (OCRe): an informatics foundation for the science of clinical research. J. Biomed. Inform. 52, 78–91 (2014)

    Article  Google Scholar 

  26. Tu, S.W., Peleg, M., Carini, S., Bobak, M., Ross, J., Rubin, D., Sim, I.: A practical method for transforming free-text eligibility criteria into computable criteria. J. Biomed. Inform. 44(2), 239–250 (2011)

    Article  Google Scholar 

  27. Bandrowski, A., Brinkman, R., Brochhausen, M., et al.: The ontology for biomedical investigations. Plos One 11(4), e0154556 (2016)

    Article  Google Scholar 

  28. Huang, X., Lin, J., Demner-Fushman, D.: Evaluation of PICO as a knowledge representation for clinical questions. Presented at the AMIA Annual Symposium Proceedings (2006)

    Google Scholar 

  29. Overell, P.: Augmented BNF for Syntax Specifications: ABNF. https://tools.ietf.org/html/rfc5234. Accessed 20 Aug 2017

  30. Hearst, M.A.: Untangling text data mining. In: 37th the Association for Computational Linguistics on Computational Linguistics meeting, pp. 3–10 (1999)

    Google Scholar 

  31. Rindflesch, T.C., Pakhomov, S.V., Fiszman, M., Kilicoglu, H., Sanchez, V.R.: Medical facts to support inferencing in natural language processing. Presented at the AMIA Annual Symposium Proceedings (2005)

    Google Scholar 

  32. O’Connor, G.T., Caffo, B., Newman, A.B., Quan, S.F., Rapoport, D.M., Redline, S., Resnick, H.E., Samet, J., Shahar, E.: Prospective study of sleep-disordered breathing and hypertension: the sleep heart health study. Am. J. Respir. Crit. Care Med. 179(12), 1159–1164 (2009)

    Article  Google Scholar 

Download references

Acknowledgement

This work is supported in part by the National Institutes of Biomedical Imaging and Bioengineering (NIBIB) Big Data to Knowledge (BD2K) grant (1U01EB020955) NSF grant# 1636850

Author information

Authors and Affiliations

  1. Institute for Computational Biology and Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA

    Joshua Valdez, Sara Arabyarmohammadi & Satya S. Sahoo

  2. Department 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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Rueschman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sara Arabyarmohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Susan Redline
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Satya S. Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satya S. Sahoo .

Editor information

Editors and Affiliations

  1. University of Lorraine, Nancy, France

    Hervé Panetto

  2. Odisee University College, Brussels, Belgium

    Christophe Debruyne

  3. Télécom SudParis, Évry, France

    Walid Gaaloul

  4. Tilburg University, Tilburg, The Netherlands

    Mike Papazoglou

  5. Freie Universität Berlin and Fraunhofer FOKUS, Berlin, Germany

    Adrian Paschke

  6. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  7. TU Graz, Graz, Austria

    Robert Meersman

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Valdez, J., Rueschman, M., Kim, M., Arabyarmohammadi, S., Redline, S., Sahoo, S.S. (2017). An Extensible Ontology Modeling Approach Using Post Coordinated Expressions for Semantic Provenance in Biomedical Research. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10574. Springer, Cham. https://doi.org/10.1007/978-3-319-69459-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69459-7_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69458-0

  • Online ISBN: 978-3-319-69459-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation