Scientometrics

, Volume 89, Issue 1, pp 349–364 | Cite as

Science models as value-added services for scholarly information systems

  • Peter Mutschke
  • Philipp Mayr
  • Philipp Schaer
  • York Sure
Article

Abstract

The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.

Keywords

Retrieval system Value-added services Science models IR Re-ranking Evaluation 

References

  1. Al-Maskari, A., Sanderson, M., & Clough, P. (2008). Relevance judgments between TREC and Non-TREC assessors. Proceedings of SIGIR, 2009, 683–684.CrossRefGoogle Scholar
  2. Alonso, O., & Mizzaro, S. (2009). Can we get rid of TREC assessors? Using Mechanical Turk for relevance assessment. In Proceedings of the SIGIR 2009 workshop on the future of IR evaluation (pp. 15–16).Google Scholar
  3. Barabasi, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A, 311, 590–614.MathSciNetMATHCrossRefGoogle Scholar
  4. Bassecoulard, E., Lelu, A., & Zitt, M. (2007). A modular sequence of retrieval procedures to delineate a scientific field: from vocabulary to citations and back. In E. Torres-Salinas & H. F. Moed (Eds.), Proceedings of the 11th international conference on scientometrics and informetrics (ISSI 2007), Madrid, Spain, 25–27 June 2007 (pp. 74–84).Google Scholar
  5. Bates, M. J. (1990). Where should the person stop and the information search interface start? Information Processing & Management, 26, 575–591.CrossRefGoogle Scholar
  6. Bates, M. J. (2002). Speculations on browsing, directed searching, and linking in relation to the Bradford distribution. Paper presented at the Fourth International Conference on Conceptions of Library and Information Science (CoLIS 4).Google Scholar
  7. Bavelas, A. (1948). A mathematical model for group structure. Applied Anthropology, 7, 16–30.Google Scholar
  8. Beaver, D. (2004). Does collaborative research have greater epistemic authority? Scientometrics, 60(3), 309–408.CrossRefGoogle Scholar
  9. Belkin, N. J. (1980). Anomalous states of knowledge as a basis for information retrieval. Canadian Journal of Information Science, 5, 133–143.Google Scholar
  10. Blair, D. C. (1990). Language and representation in information retrieval. Amsterdam, NY: Elsevier.Google Scholar
  11. Blair, D. C. (2002). The challenge of commercial document retrieval. Part II. A strategy for document searching based on identifiable document partitions. Information Processing and Management, 38(2), 293–304.MATHCrossRefGoogle Scholar
  12. Blair, D. C. (2003). Information retrieval and the philosophy of language. Annual Review of Information Science and Technology, 37, 3–50.CrossRefGoogle Scholar
  13. Börner, K., & Scharnhorst, A. (2009). Visual conceptualizations and models of science. Journal of Informetrics, 3, 161–172.CrossRefGoogle Scholar
  14. Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? JASIST, 61(12), 2389–2404.CrossRefGoogle Scholar
  15. Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137(3550), 85–86.Google Scholar
  16. Bradford, S. C. (1948). Documentation. London: Lockwood.Google Scholar
  17. Brookes, B. C. (1977). Theory of the Bradford Law. Journal of Documentation, 33(3), 180–209.CrossRefGoogle Scholar
  18. Buckland, M., Chen, A., Chen, H.-M., Kim, Y., Lam, B., Larson, R., et al. (1999). Mapping entry vocabulary to unfamiliar metadata vocabularies. D-Lib Magazine, 5(1).Google Scholar
  19. Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.CrossRefGoogle Scholar
  20. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3, 191–209.CrossRefGoogle Scholar
  21. Efthimiadis, E. N. (1996). Query expansion. In M. E. Williams (Ed.), Annual review of information systems and technology (ARIST) (Vol. 31, pp. 121–187). Information Today.Google Scholar
  22. Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378–382.CrossRefGoogle Scholar
  23. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.CrossRefGoogle Scholar
  24. Freeman, L. C. (1978/1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215–239.Google Scholar
  25. Freeman, L. C. (1980). The gatekeeper, pair-dependency and structural centrality. Quality & Quantity, 14, 585–592.CrossRefGoogle Scholar
  26. Fuhr, N., Schaefer, A., Klas, C.-P., & Mutschke, P. (2002). Daffodil: An integrated desktop for supporting high-level search activities in federated digital libraries. In M. Agosti & C. Thanos (Eds.), Research and advanced technology for digital libraries. 6th European conference, EDCL 2002, proceedings (pp. 597–612). Berlin: Springer-Verlag.Google Scholar
  27. Glänzel, W., Janssens, F., & Thijs, B. (2009). A comparative analysis of publication activity and citation impact based on the core literature in bioinformatics. Scientometrics, 79(1), 109–129.CrossRefGoogle Scholar
  28. He, Z.-L. (2009). International collaboration does not have greater epistemic authority. JASIST, 60(10), 2151–2164.CrossRefGoogle Scholar
  29. Hjørland, B., & Nicolaisen, J. (2005). Bradford’s law of scattering: ambiguities in the concept of “subject”. Paper presented at the 5th International Conference on Conceptions of Library and Information Science.Google Scholar
  30. Huberman, B. A., & Adamic, L. A. (2004). Information dynamics in the networked world. Lect. Notes Phys. (Vol. 650, pp. 371–398).Google Scholar
  31. Jiang, Y. (2008). Locating active actors in the scientific collaboration communities based on interaction topology analysis. Scientometrics, 74(3), 471–482.CrossRefGoogle Scholar
  32. Lang, F. R., & Neyer, F. J. (2004). Kooperationsnetzwerke und Karrieren an deutschen Hochschulen. KfZSS, 56(3), 520–538.Google Scholar
  33. Leydesdorff, L., de Moya-Anegón, F., & Guerrero-Bote, V. P. (2010). Journal maps on the basis of Scopus data: A comparison with the Journal Citation Reports of the ISI. JASIST, 61(2), 352–369.Google Scholar
  34. Leydesdorff, L., & Wagner, C. S. (2008). International collaboration in science and the formation of a core group. Journal of Informetrics, 2(4), 317–325.CrossRefGoogle Scholar
  35. Liu, X., Bollen, J., Nelson, M. L., & van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing and Management, 41(2005), 1462–1480.CrossRefGoogle Scholar
  36. Lu, H., & Feng, Y. (2009). A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships. Scientometrics, 81(2), 499–511.MathSciNetCrossRefGoogle Scholar
  37. Mayr, P. (2008). An evaluation of Bradfordizing effects. In Proceedings of WIS 2008, Berlin, fourth international conference on webometrics, informetrics and scientometrics & ninth COLLNET meeting. Humboldt-Universität zu Berlin.Google Scholar
  38. Mayr, P. (2009). Re-Ranking auf Basis von Bradfordizing für die verteilte Suche in Digitalen Bibliotheken. Berlin: Humboldt-Universität zu Berlin.Google Scholar
  39. Mayr, P., Mutschke, P., & Petras, V. (2008). Reducing semantic complexity in distributed digital libraries: Treatment of term vagueness and document re-ranking. Library Review, 57(3), 213–224.CrossRefGoogle Scholar
  40. Mitra, M., Singhal, A., & Buckley C. (1998). Improving automatic query expansion. In Proceedings of SIGIR (pp. 206–214).Google Scholar
  41. Mutschke, P. (1994). Processing scientific networks in bibliographic databases. In H. H. Bock, et al. (Eds.), Information systems and data analysis. Prospects–foundations–applications. Proceedings 17th annual conference of the GfKl 1993 (pp. 127–133). Heidelberg: Springer-Verlag.Google Scholar
  42. Mutschke, P. (2001). Enhancing information retrieval in federated bibliographic data sources using author network based stratagems. In P. Constantopoulos & I. T. Sölvberg (Eds.), Research and advanced technology for digital libraries: 5th European conference, ECDL 2001, Proceedings (Vol. 2163, pp. 287–299). Notes in Computer Science. Berlin: Springer-Verlag.Google Scholar
  43. Mutschke, P. (2004a). Autorennetzwerke: Verfahren der Netzwerkanalyse als Mehrwertdienste für Informationssysteme. Bonn: Informationszentrum Sozialwissenschaften (IZ-Arbeitsbericht Nr. 32).Google Scholar
  44. Mutschke, P. (2004b). Autorennetzwerke: Netzwerkanalyse als Mehrwertdienst für Informationssysteme. In B. Bekavac, et al. (Eds.), Information zwischen Kultur und Marktwirtschaft: Proceedings des 9. Internationalen Symposiums für Informationswissenschaft (ISI 2004) (pp. 141–162). Konstanz: UVK Verl.-Ges.Google Scholar
  45. Mutschke, P. (2010). Zentralitäts- und Prestigemaße. In R. Häußling & C. Stegbauer (Eds.), Handbuch Netzwerkforschung (pp. 365–378). Wiesbaden: VS-Verlag für Sozialwissenschaften.CrossRefGoogle Scholar
  46. Mutschke, P., & Quan-Haase, A. (2001). Collaboration and cognitive structures in social science research fields: Towards socio-cognitive analysis in information systems. Scientometrics, 52(3), 487–502.CrossRefGoogle Scholar
  47. Mutschke, P., & Renner, I. (1995). Akteure und Themen im Gewaltdiskurs: Eine Strukturanalyse der Forschungslandschaft. In E. Mochmann & U. Gerhardt (Eds.), Gewalt in Deutschland: Soziale Befunde und Deutungslinien (pp. 147–192). Munich: Oldenburg Verlag.Google Scholar
  48. Newman, M. E. J. (2001). The structure of scientific collaboration networks. PNAS, 98, 404–409.MATHCrossRefGoogle Scholar
  49. Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. PNAS, 101, 5200–5205.CrossRefGoogle Scholar
  50. Petras, V. (2006). Translating dialects in search: Mapping between specialized languages of discourse and documentary languages. Berkley: University of California.Google Scholar
  51. Plaunt, C., & Norgard, B. A. (1998). An association based method for automatic indexing with a controlled vocabulary. Journal of the American Society for Information Science, 49(August 1998), 888–902.Google Scholar
  52. Schaer, P., Mayr, P., & Mutschke, P. (2010). Implications of inter-rater agreement on a student information retrieval evaluation. In M. Atzmüller, et al. (Eds.), Proceedings of LWA2010—Workshop-Woche: Lernen, Wissen & Adaptivität.Google Scholar
  53. Shiri, A., & Revie, C. (2006). Query expansion behavior within a thesaurus-enhanced search environment: A user-centered evaluation. JASIST, 57(4), 462–478.CrossRefGoogle Scholar
  54. Sonnewald, D. H. (2007). Scientific collaboration. Annual Review of Information Science & Technology, 41(1), 643–681.Google Scholar
  55. Voorhees, E. M., & Harman, D. K. (Eds.). (2005). TREC: Experiment and evaluation in information retrieval. Cambridge, MA: The MIT Press.Google Scholar
  56. White, H. D. (1981). ‘Bradfordizing’ search output: how it would help online users. Online Review, 5(1), 47–54.CrossRefGoogle Scholar
  57. White, R. W., & Marchionini, G. (2007). Examining the effectiveness of real-time query expansion. Information Processing & Management, 43(3), 685–704.CrossRefGoogle Scholar
  58. Worthen, D. B. (1975). The application of Bradford’s law to monographs. Journal of Documentation, 31(1), 19–25.CrossRefGoogle Scholar
  59. Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. JASIST, 60(10), 21-07-2118.Google Scholar
  60. Yin, L., Kretschmer, H., Hannemann, R. A., & Liu, Z. (2006). Connection and stratification in research collaboration: An analysis of the COLLNET network. Information Processing & Management, 42, 1599–1613.CrossRefGoogle Scholar
  61. Zhou, D., Orshansky, S. A., Zha, H., & Giles, C. L. (2007). Co-ranking authors and documents in a heterogeneous network. In Proceedings of the 2007 seventh IEEE international conference on data mining (pp. 739–744).Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  • Peter Mutschke
    • 1
  • Philipp Mayr
    • 1
  • Philipp Schaer
    • 1
  • York Sure
    • 1
  1. 1.GESIS-Leibniz Institute for the Social SciencesBonnGermany

Personalised recommendations