A Linked-Data-Driven and Semantically-Enabled Journal Portal for Scientometrics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8219)


The Semantic Web journal by IOS Press follows a unique open and transparent process during which each submitted manuscript is available online together with the full history of its successive decision statuses, assigned editors, solicited and voluntary reviewers, their full text reviews, and in many cases also the authors’ response letters. Combined with a highly-customized, Drupal-based journal management system, this provides the journal with semantically rich manuscript time lines and networked data about authors, reviewers, and editors. These data are now exposed using a SPARQL endpoint, an extended Bibo ontology, and a modular Linked Data portal that provides interactive scientometrics based on established and new analysis methods. The portal can be customized for other journals as well.


Resource Description Framework Link Data Latent Dirichlet Allocation SPARQL Query Bibliographic Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Stadler, C., Lehmann, J., Höffner, K., Auer, S.: LinkedGeoData: A core for a web of spatial open data. Semantic Web 3(4), 333–354 (2012)Google Scholar
  2. 2.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)Google Scholar
  3. 3.
    Janowicz, K., Hitzler, P.: Open and transparent: the review process of the Semantic Web journal. Learned Publishing 25(1), 48–55 (2012)CrossRefGoogle Scholar
  4. 4.
    Hitzler, P., Janowicz, K., Sengupta, K.: The new manuscript review system for the Semantic Web journal. Semantic Web 4(2), 117 (2013)Google Scholar
  5. 5.
    D’Arcus, B., Giasson, F.: Bibliographic Ontology Specification (November 2009), (last accessed on May 12, 2013)
  6. 6.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  7. 7.
    Shotton, D., Portwin, K., Klyne, G., Miles, A.: Adventures in semantic publishing: exemplar semantic enhancements of a research article. PLoS Computational Biology 5(4), e1000361 (2009)Google Scholar
  8. 8.
    Keßler, C., Janowicz, K., Kauppinen, T.: spatial@linkedscience – Exploring the Research Field of GIScience with Linked Data. In: Xiao, N., Kwan, M.-P., Goodchild, M.F., Shekhar, S. (eds.) GIScience 2012. LNCS, vol. 7478, pp. 102–115. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Hood, W.W., Wilson, C.S.: The literature of bibliometrics, scientometrics, and informetrics. Scientometrics 52(2), 291–314 (2001)CrossRefGoogle Scholar
  10. 10.
    Braun, T., Glänzel, W., Schubert, A.: A Hirsch-type index for journals. Scientometrics 69(1), 169–173 (2006)CrossRefGoogle Scholar
  11. 11.
    Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America 102(46), 16569 (2005)CrossRefGoogle Scholar
  12. 12.
    Glenisson, P., Glänzel, W., Janssens, F., De Moor, B.: Combining full text and bibliometric information in mapping scientific disciplines. Information Processing & Management 41(6), 1548–1572 (2005)CrossRefGoogle Scholar
  13. 13.
    Brody, T., Carr, L., Gingras, Y., Hajjem, C., Harnad, S., Swan, A.: Incentivizing the open access research web: publication-archiving, data-archiving and scientometrics. CTWatch Quarterly 3(3) (2007)Google Scholar
  14. 14.
    Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)Google Scholar
  15. 15.
    Wang, X., McCallum, A.: Topics over time: a non-markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433. ACM (2006)Google Scholar
  16. 16.
    Zhou, D., Ji, X., Zha, H., Giles, C.L.: Topic evolution and social interactions: how authors effect research. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 248–257. ACM (2006)Google Scholar
  17. 17.
    Bornmann, L., Waltman, L.: The detection of “hot regions” in the geography of science — A visualization approach by using density maps. Journal of Informetrics 5(4), 547–553 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Wright State UniversityDaytonUSA

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