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Factors Influencing the Adoption of the Semantic Web in the Life Sciences

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Semantic Web

Abstract

The Semantic Web today is a vision of transparent search, request, manipulation, and delivery of information to the user by an interconnected set of services. This vision would change the way scientists interact with data, computations, and even each other. Realizing it begins with understanding the needs of biologists and the dynamic continuum of factors that will determine whether, in what form, and at what rate the Semantic Web is likely to be adopted as a scientific tool by this community. In this chapter I look at this continuum and hazard some predictions.

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References

  1. Altman R.B., Klein T.E., Murray T., and Dunker A.K., editors. Pacific Symposium on Biocomputing, 2006, Singapore, 2006. World Scientific Publishing Co.

    Google Scholar 

  2. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J.C., Richardson J.E., Ringwald M, Rubin G.M., and Sherlock G. Gene Ontology: tool for the unification of biology. Nature Genet., 25:25–29, 2000.

    Article  PubMed  CAS  Google Scholar 

  3. Bader G.D. and Hogue C.W.V. BIND — a data specification for storing and describing biomolecular interactions, molecular complexes and pathways. Bioinformatics, 16:465–477, 2000.

    Article  PubMed  CAS  Google Scholar 

  4. Beckett D., editor. RDF/XML Syntax Specification (Revised). W3C, 2004. http://www.w3.org/TR/2004/REC-rdf-syntax-grammar-20040210/.

    Google Scholar 

  5. Berners-Lee T. Semantic Web Road Map, 1998. W3C, http://www.w3.org/DesignIssues/Semantic.html.

    Google Scholar 

  6. BioPAX Group. BioPAX: Biological Pathways Exchange, 2002. www.biopax.org/.

    Google Scholar 

  7. Brickley D. and Guha R.V., editors. RDF Vocabulary Description Language 1.0: RDF Schema, 2004. W3C, http://www.w3c.org/TR/rdf-schema.

    Google Scholar 

  8. Carroll J.J. and De Roo J., editors. OWL Web Ontology Language Test Cases. W3C, 2004. http://www.w3.org/TR/2004/REC-owl-test-20040210/.

    Google Scholar 

  9. Cerami E. Web Services Essentials. O’Reilly and Associates, Inc., Sebastopol CA, 2002.

    Google Scholar 

  10. Eclipse.org. eclipse, 2006. http://www.eclipse.org/.

    Google Scholar 

  11. Gene Ontology Consortium. 2003. http://www.geneontology.org/.

    Google Scholar 

  12. Gennari J., Musen M.A., Fergerson R.W., Grosso W.E., Crubezy M., Eriksson H., Noy N.F., and Tu S.W. The evolution of Protégé: an environment for knowledge-based systems development. Technical report, Stanford University SMI-2002=0943, 2002. http://protege.Stanford.edu/doc/auslese/-smi-web/research/details.jsp?PubId=0943.

    Google Scholar 

  13. Globus Team. The Globus Project, 2003. http://www.globus.org/.

    Google Scholar 

  14. Harold E.R. and Means W.S. XML in a Nutshell. O’Reilly and Associates, Inc., Sebastopol CA, second edition, 2002.

    Google Scholar 

  15. Hayes P., editor. RDF Semantics. W3C, 2004. http://www.w3.org/TR/2004/REC-rdf-mt-20040210/.

    Google Scholar 

  16. Hucka M., Finney A., Bornstein B.J., Keating S.M., Shapiro B.E., Matthews J., Kovita B.L., Schilstra M.J., Funahashi A., Doyce J.C., and Kitano H. Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. Sys. Biol., 1:41–53, 2004.

    Article  CAS  Google Scholar 

  17. International Union of Biochemistry and Molecular Biology. Enzyme Nomenclature. Recommendations (1992) of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Academic Press, Inc., London, 1992.

    Google Scholar 

  18. Katz M.L. and Shapiro C. Network externalities, competition, and compatibility. Am. Econ. Rev., 75:424–440, 1985.

    Google Scholar 

  19. Katz M.L. and Shapiro C. Product introduction with network externalities. J. Ind. Econ., 40:55–83, 1992.

    Article  Google Scholar 

  20. Kazic T. Representation, reasoning and the intermediary metabolism of Escherichia coli. In Trevor N. Mudge, Veljko Milutinovic, and Lawrence Hunter, editors, Proceedings of the Twenty-Sixth Annual Hawaii International Conference on System Sciences, volume 1, pages 853–862, Los Alamitos CA, 1993. IEEE Computer Society Press.

    Google Scholar 

  21. Kazic T. Semiotes — a semantics for sharing. Bioinformatics, 16:1129–1144, 2000.

    Article  PubMed  CAS  Google Scholar 

  22. Kazic T. Putting semantics into the Semantic Web: how well can it capture biology? In Klein T.E., Murray T., and Dunker A.K., editors. Pacific Symposium on Biocomputing, 2006, Singapore, 2006 Altman et al. [1], pages 140–151.

    Google Scholar 

  23. Lloyd C.M., Halstead M.D.B., and Nielsen P.F. CellML: its future, present, and past. Prog. Biophys. Mol. Biol., 85:433–450, 2004.

    Article  PubMed  CAS  Google Scholar 

  24. Lutz C, editor. Description Logics, 2005. http://dl.kr.org.

    Google Scholar 

  25. MaizeGDB. MaizeGDB, 2003. Iowa State University, http://www.maizegdb.org/.

    Google Scholar 

  26. McGuinness D.L. and van Harmelen F., editors. OWL Web Ontology Language Overview. W3C, 2004. http://www.w3.org/TR/2004/REC-owl-features-20040210/.

    Google Scholar 

  27. Microarray Gene Expression Data Society. MGED Home. Microarray Gene Expression Data Society, 2002. http://www.mged.org/.

    Google Scholar 

  28. National Center for Biomedical Ontology. OBO: open biomedical ontologies. National Center for Biomedical Ontology, 2005. http://obo.sourceforge.net/.

    Google Scholar 

  29. National Library of Medicine. National Library of Medicine Fact Sheet: UMLS Semantic Network. National Library of Medicine, 1999. http://www.nlm.nih.gov/pubs/factsheets/umlssemn.html.

    Google Scholar 

  30. Neumann E.K. and Quan D. BioDASH: a Semantic Web dashboard for drug development. In Klein T.E., Murray T., and Dunker A.K., editors. Pacific Symposium on Biocomputing, 2006, Singapore, 2006 Altman et al. [1], pages 176–187.

    Google Scholar 

  31. Ostell J. NCBI ASN.1 specifications. 1990. ftp://ncbi.nlm.nih.gov/repository/swiss-prot/asn/asn.all.

    Google Scholar 

  32. Patel-Schneider P.F. and Horrocks I., editors. OWL Web Ontology Language Semantics and Abstract Syntax, 2004. W3C, http://www.w3.org/TR/owl-semantics/.

    Google Scholar 

  33. Plant Ontology Consortium. Plant Ontology. Plant Ontology Consortium, 2003. http://www.plantontology.org/.

    Google Scholar 

  34. PredictProtein Team. PredictProtein. Columbia University, 2003. http://www.predictprotein.org/.

    Google Scholar 

  35. Rosse C. and Mejino J.L.V. A reference ontology for bioinformatics: the Foundational Model of Anatomy. J. Biomed. Inform., 36:478–500, 2003.

    Article  PubMed  Google Scholar 

  36. Rost B., Yachdav G., and Liu J. The PredictProtein server. Nucleic Acids Res., 32:W321–W326, 2003.

    Article  Google Scholar 

  37. Sheth A.P. and Larson J.A. Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Comp. Surv., 22:183–236, 1990.

    Article  Google Scholar 

  38. Snell J., Tidwell D., and Kulchenko P. Programming Web Services with SOAP. O’Reilly and Associates, Inc., Sebastopol CA, 2002.

    Google Scholar 

  39. Subramaniam S. The Biology Workbench: a seamless database and analysis environment for the biologist. Proteins, 32:1–2, 1998.

    Article  PubMed  CAS  Google Scholar 

  40. Weinberg B.A. Experience and technology adoption. discussion paper no. 1051. Technical report, Institute for the Study of Labor (IZA), Bonn, 2004.

    Google Scholar 

  41. Wilshire Conferences. Semantic Technology Conference. In 2006 Semantic Technology Conference, 2006. http://www.semantic-conference.com/. Wilshire Conferences.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Missouri, USA

    Toni Kazic

Authors
  1. Toni Kazic
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Editor information

Editors and Affiliations

  1. Knowledge Discovery Department, Institute for Infocomm Research, Singapore

    Christopher J. O. Baker

  2. Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA

    Kei-Hoi Cheung

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Kazic, T. (2007). Factors Influencing the Adoption of the Semantic Web in the Life Sciences. In: Baker, C.J.O., Cheung, KH. (eds) Semantic Web. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-48438-9_18

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