Advertisement

Scientometrics

, Volume 80, Issue 2, pp 385–406 | Cite as

Differentiating, describing, and visualizing scientific space: A novel approach to the analysis of published scientific abstracts

  • Eli M. BlattEmail author
Article

Abstract

This paper will develop and demonstrate a novel method for analyzing scientific indexes called Latent Semantic Differentiation. Using two distinct datasets comprised of scientific abstracts, it will demonstrate the procedure’s ability to identify the dominant themes, cluster the articles accordingly, visualize the results, and provide a qualitative description of each cluster. Combined, the analyses will highlight the utility of the procedure for scientific document indexing, structuring university departments, facilitating grant administration, and augmenting ongoing research on scientific citation. Because the procedure is extensible to any textual domain, there are numerous avenues for continued research both within the sciences and beyond.

Keywords

Citation Analysis Latent Semantic Analysis Semantic Space Dissimilarity Matrix Dominant Theme 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kuhn, T. S., The Structure of Scientific Revolutions, The University of Chicago Press, Chicago, 1996.Google Scholar
  2. 2.
    Braam, R. R., H. F. Moed, A. F. J. Vanraan, Mapping of science by combined cocitation and word analysis. 1. Structural aspects, Journal of the American Society for Information Science, 42(4) (1991) 233–251.CrossRefGoogle Scholar
  3. 3.
    Blute, M., The evolutionary ecology of science, Journal of Memetics-Evolutionary Models of Information Transmission, 7(1) (2003) 19.Google Scholar
  4. 4.
    Hull, D., Science as a Process, The University of Chicago Press, Chicago, 1988.Google Scholar
  5. 5.
    Leydesdorff, L., Theories of citation?, Scientometrics, 43(1) (1998) 5–25.CrossRefGoogle Scholar
  6. 6.
    White, H. D., K. W. Mccain, Visualizing a discipline: An author co-citation analysis of information science, 1972–1995, Journal of the American Society for Information Science, 49(4) (1998) 327–355.Google Scholar
  7. 7.
    Shiffrin, R. M., K. Borner, Mapping knowledge domains, Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl. 1) (2004) 5183–5185.CrossRefGoogle Scholar
  8. 8.
    Soderqvist, T., A. M. Silverstein, Studying leadership and subdisciplinary structure of scientific disciplines — Cluster-analysis of participation in scientific meetings, Scientometrics, 30(1) (1994) 243–258.CrossRefGoogle Scholar
  9. 9.
    Newman, M. E. J., Coauthorship networks and patterns of scientific collaboration, PNAS, 101(Suppl. 1) (2004) 5200–5205.CrossRefGoogle Scholar
  10. 10.
    Morris, S. A., G. G. Yen, Crossmaps: Visualization of overlapping relationships in collections of journal papers, PNAS, 101(Suppl. 1) (2004) 5291–5296.CrossRefGoogle Scholar
  11. 11.
    Hopcroft, J. J., Khan, O., Kulis, B., Selman, B., Tracking evolving communities in large linked networks, Proceedings of the National Academy of Sciences, 101(1) (2004) 5249–5253.CrossRefGoogle Scholar
  12. 12.
    Mika, P., T. Elfring, P. Groenewegen, Application of semantic technology for social network analysis in the sciences, Scientometrics, 68(1) (2006) 3–27.CrossRefGoogle Scholar
  13. 13.
    White, H. D., et al., User-controlled mapping of significant literatures, PNAS, 101(Suppl. 1) (2004) 5297–5302.CrossRefGoogle Scholar
  14. 14.
    Skupin, A., The world of geography: Visualizing a knowledge domain with cartographic means, PNAS, 101(Suppl. 1) (2004) 5274–5278.CrossRefGoogle Scholar
  15. 15.
    Menczer, F., Evolution of document networks, PNAS, 101(Suppl. 1) (2004) 5261–5265.CrossRefGoogle Scholar
  16. 16.
    Mane, K. K., K. Borner, Mapping topics and topic bursts in PNAS, PNAS, 101(Suppl. 1) (2004) 5287–5290.CrossRefGoogle Scholar
  17. 17.
    Hui, S. C., A. C. M., Document retrieval from a citation database using conceptual clustering and co-word analysis., Online Information Review, 28(1) (2004) 22–32.CrossRefGoogle Scholar
  18. 18.
    Henzinger, M., S. Lawrence, Extracting knowledge from the world wide web, PNAS, 101(Suppl. 1) (2004) 5186–5191.CrossRefGoogle Scholar
  19. 19.
    Ginsparg, P., et al., Mapping subsets of scholarly information, PNAS, 101(Suppl. 1) (2004) 5236–5240.CrossRefGoogle Scholar
  20. 20.
    Erosheva, E., S. Fienberg, J. Lafferty, Mixed-membership models of scientific publications, PNAS, 101(Suppl. 1) (2004) 5220–5227.CrossRefGoogle Scholar
  21. 21.
    Griffiths, T. L., M. Steyvers, Finding scientific topics, PNAS, 101(Suppl. 1) (2004) 5228–5235.CrossRefGoogle Scholar
  22. 22.
    Landauer, T. K., D. Laham, M. Derr From paragraph to graph: Latent semantic analysis for information visualization, Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl. 1) (2004) 5214–5219.CrossRefGoogle Scholar
  23. 23.
    Landauer, T. K., P. W. Foltz, D. Laham, An introduction to latent semantic analysis, Discourse Processes, 25(2&3) (1998) 259–284.CrossRefGoogle Scholar
  24. 24.
    Landauer, T. K., S. T. Dumais, A Solution to plato’s cave: The latent semantic analysis theory of acquisition, induction, and representation of knowledge, Psychological Review, 104(2) (1997) 211–240.CrossRefGoogle Scholar
  25. 25.
    Landauer, T. K., Learning and representing verbal meaning: The latent semantic analysis theory, Current Directions in Psychological Science, 7(5) (1998) 161–164.CrossRefGoogle Scholar
  26. 26.
    Best, M., R. Pocklington Meaning as use: Transmission fidelity and evolution in the NetNews, Journal of Theoretical Biology, 196(3) (1999) 389–395.CrossRefGoogle Scholar
  27. 27.
    Laham, D., “Latent Semantic Analysis Approaches to Categorization.” From http://scholar.google.com/url?sa=U&q=http://lsa.colorado.edu/categories.pdf, 1997.
  28. 28.
    Berry, M. W., L. Wo, J. T. Giles, GTP (General Text Parser) Software for Text Mining. C. Warren Neel Conference on the new frontiers of statistical data mining and knowledge discovery, Knoxville, TX, 2002.Google Scholar
  29. 29.
    Best, M., Microevolutionary Language Theory. Massachusetts Institute of Technology, City, 2000.Google Scholar
  30. 30.
    Landauer, T. K., Data Requirements for Conducting Latent Semantic Analysis. City, 2006.Google Scholar
  31. 31.
    Shenk, M., Models for the Future of Anthropology. City, 2006.Google Scholar
  32. 32.
    Alden Smith, E.Anthropological Schisms. City, 2006.Google Scholar
  33. 33.
    Yanagisako, S., D. Segal, Welcoming Debate: Exploring Links and Disconnects Among the Quadrants. City, 2006.Google Scholar
  34. 34.
    van Leeuwen, T.N., et al., Language biases in the coverage of the Science Citation Index and its consequences for international comparisons of national research performance, Scientometrics, 51(1) (2001) 335–346.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2009

Authors and Affiliations

  1. 1.Department of Anthropological SciencesStanford UniversityStanfordUSA

Personalised recommendations