Accelerating the Exploitation of (bio)medical Knowledge Using Linked Data

  • Mohammad ShafahiEmail author
  • Hamideh Afsarmanesh
  • Hayo Bart
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 690)


Early identification and treatment of a diseases, especially when chronic, can reduce severe complications for the patients, doctors, and the society as a whole. Therefore, becoming aware and having insight about the state of the art findings on diseases, if communicated properly to different stakeholders, will benefit all. The medical research field, however, is vast and dynamically evolves with new discoveries. Additionally, new results are being continuously generated. The new discoveries on diseases address their diagnosis, prognosis, and possible treatment pathways for each disease, which are typically published in medical articles. Research results, however, are not reflected in practice by practitioners, unless they are officially verified by governments and authoritative health institutes, and appear in medical guidelines. Developing the medical guidelines requires identifying every relevant medical article, traversing through and validating it, as well as gathering and inter-relating that data to the information from other relevant sources, such as the drug interaction databases.

Therefore, optimal exploitation of medical advances and research results by all its stakeholders, being researchers, practitioners, and patients, is essential. This, however, is hindered due to both the lack of integration of their typically disparate information, and the lack of facilities for coherent, up-to-date, and personalized access by their stakeholders. The few researches that address these issues do not sufficiently address the needed dynamism in data, lack intuitiveness in their use, and present a rather limited amount of information, which is usually obtained from a single source. This research aims to address these gaps through the development of BioMed Xplorer, presenting a model and a tool that enables researchers to rapidly query and explore biomedical knowledge from multiple sources, while preserving provenance data, and presenting all inter-linked information through an intuitive and personalized user interface. Results are further validated by some domain experts, through contrasting it against the state of the art, and with a task-based validation experimenting with the real case of updating medical guidelines.


BioMed Xplorer Disease related information Semantic Web Knowledge base ontology Visualization Provenance data Medical knowledge External data source RDF Graph Knowledge exploration 



This work was carried out on the Dutch national e-infrastructure with the support of SURF Foundation. We also like to thank the School of Medicine at Democritus University of Trace for helping with some requirements identification and validation.


  1. 1.
    Antoniou, G., Van Harmelen, F.: A Semantic Web Primer. MIT Press, Cambridge (2004)Google Scholar
  2. 2.
    Aronson, A.: Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. In: Proceedings of AMIA Symposium, pp. 17–21 (2001)Google Scholar
  3. 3.
    Belleau, F., Nolin, M.A., Tourigny, N., Rigault, P., Morissette, J.: Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inform. 41(5), 706–716 (2008)CrossRefGoogle Scholar
  4. 4.
    Berners-Lee, T., Bizer, C., Heath, T.: Linked data-the story so far. Int. J. Seman. Web Inf. Syst. 5(3), 1–22 (2009)CrossRefGoogle Scholar
  5. 5.
    Berners-Lee, T., Hendler, J., Lassila, O., et al.: The semantic web. Sci. Am. 284(5), 28–37 (2001)CrossRefGoogle Scholar
  6. 6.
    Bizer, C., Cyganiak, R.: D2R server-publishing relational databases on the semantic web. In: Poster at the 5th International Semantic Web Conference, pp. 294–309 (2006)Google Scholar
  7. 7.
    Bizer, C., Seaborne, A.: D2RQ-treating non-RDF databases as virtual RDF graphs. In: Proceedings of the 3rd International Semantic Web Conference (ISWC 2004), vol. 2004. Citeseer, Hiroshima (2004)Google Scholar
  8. 8.
    Bodenreider, O.: A semantic navigation tool for the UMLS. In: Proceedings of the AMIA Symposium, pp. 971. American Medical Informatics Association (2000)Google Scholar
  9. 9.
    Cohen, A.M., Hersh, W.R.: A survey of current work in biomedical text mining. Briefings Bioinform. 6(1), 57–71 (2005)CrossRefGoogle Scholar
  10. 10.
    D’Arcus, B., Giasson, F.: Bibliographic Ontology Specification (BIBO) (2015). Accessed 28 Aug 2015
  11. 11.
    Dean, M., Schreiber, G., Bechhofer, S., van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F., Stein, L.A.: Owl web ontology language reference. W3C Recommendation, 10 February 2004Google Scholar
  12. 12.
    Dublin Core Metadata Initiative (DCMI): Dublin Core (DC) (2015). Accessed 28 Aug 2015
  13. 13.
    Harris, S., Seaborne, A., Prudhommeaux, E.: SPARQL 1.1 query language. W3C Recommendation 21 (2013)Google Scholar
  14. 14.
    Hendler, J.: Data integration for heterogenous datasets. Big Data 2(4), 205–215 (2014)CrossRefGoogle Scholar
  15. 15.
    Hu, Q., Huang, Z., den Teije, A., van Harmelen, F.: Detecting new evidence for evidence-based guidelines using a semantic distance method. In: Proceedings of the 15th Conference on Artificial Intelligence in Medicine (AIME 2015) (2015)Google Scholar
  16. 16.
    Hu, Q., Huang, Z., ten Teije, A., van Harmelen, F., Marshall, M., Dekker, A.: A topic-centric approach to detecting new evidences for evidence-based medical guidelines. In: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (HealthInf 2016) (2016)Google Scholar
  17. 17.
    Hunter, L., Cohen, K.B.: Biomedical language processing: what’s beyond PubMed? Mol. Cell 21(5), 589–594 (2006)Google Scholar
  18. 18.
    Institute of Applied Informatics and Formal Description Methods - Karlsruhe Research Institute: (2015). Accessed 28 Aug 2015
  19. 19.
    Johns Hopkins University: Online Mendelian Inheritance in Man (OMIM) (2015). Accessed 28 Aug 2015
  20. 20.
    Kilicoglu, H., Fiszman, M., Rodriguez, A., Shin, D., Ripple, A., Rindflesch, T.C.: Semantic MEDLINE: a web application for managing the results of PubMed searches. In: Proceedings of the Third International Symposium for Semantic Mining in Biomedicine, vol. 2008, pp. 69–76. Citeseer (2008)Google Scholar
  21. 21.
    Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., Rindflesch, T.C.: SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics 28(23), 3158–3160 (2012)CrossRefGoogle Scholar
  22. 22.
    Lu, Z.: PubMed and beyond: a survey of web tools for searching biomedical literature. Database 2011, baq036 (2011)Google Scholar
  23. 23.
    Momtchev, V., Peychev, D., Primov, T., Georgiev, G.: Expanding the pathway and interaction knowledge in linked life data. In: Proceedings of International Semantic Web Challenge (2009)Google Scholar
  24. 24.
    NABON: Breast cancer, dutch guideline, version 2.0. Tech. rep., Integraal kankercentrum Netherland, Nationaal Borstkanker Overleg Nederland (2012)Google Scholar
  25. 25.
    Plake, C., Schiemann, T., Pankalla, M., Hakenberg, J., Leser, U.: AliBaba: PubMed as a graph. Bioinformatics 22(19), 2444–2445 (2006)CrossRefGoogle Scholar
  26. 26.
    Rebholz-Schuhmann, D., Kirsch, H., Arregui, M., Gaudan, S., Riethoven, M., Stoehr, P.: EBIMed - text crunching to gather facts for proteins from MEDLINE. Bioinformatics 23(2), e237–e244 (2007)CrossRefGoogle Scholar
  27. 27.
    Shadbolt, N., Hall, W., Berners-Lee, T.: The semantic web revisited. IEEE Intell. Syst. 21(3), 96–101 (2006)CrossRefGoogle Scholar
  28. 28.
    Tao, C., Zhang, Y., Jiang, G., Bouamrane, M.M., Chute, C.G.: Optimizing semantic MEDLINE for translational science studies using semantic web technologies. In: Proceedings of the 2nd International Workshop on Managing Interoperability and compleXity in Health Systems, pp. 53–58. ACM (2012)Google Scholar
  29. 29.
    Tao, Y., Friedman, C., Lussier, Y.A.: Visualizing information across multidimensional post-genomic structured and textual databases. Bioinformatics 21(8), 1659–1667 (2005)CrossRefGoogle Scholar
  30. 30.
    U.S. National Cancer Institute: NCI Metathesaurus (NCIm) (2015). Accessed 28 Aug 2015
  31. 31.
    U.S. National Library of Medicine: Medline fact sheet (2015). Accessed 28 Aug 2015
  32. 32.
    U.S. National Library of Medicine: Medline Plus (2015). Accessed 28 Aug 2015
  33. 33.
    U.S. National Library of Medicine: SemRep (2015). Accessed 28 Aug 2015
  34. 34.
    U.S. National Library of Medicine: Unified Medical Language System (UMLS) (2015). Accessed 28 Aug 2015
  35. 35.
    WebMD, LLC: WebMD (2015). Accessed 28 Aug 2015
  36. 36.
    Weibel, S.L., Jul, E., Shafer, K.E.: PURLs: Persistent Uniform Resource Locators. OCLC Online Computer Library Center (1996)Google Scholar
  37. 37.
    Weizmann Institute of Science: MalaCards (2015). Accessed 28 Aug 2015
  38. 38.
    World Wide Web Consortium: Simple Knowledge Organization System (SKOS) (2015). Accessed 28 Aug 2015
  39. 39.
    World Wide Web Consortium: RDF 1.1 Semantics. World Wide Web Consortium (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohammad Shafahi
    • 1
    Email author
  • Hamideh Afsarmanesh
    • 1
  • Hayo Bart
    • 1
  1. 1.Faculty of Science, Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands

Personalised recommendations