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Science models as value-added services for scholarly information systems

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.

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Notes

  1. 1.

    http://www.gesis.org/irm/.

  2. 2.

    Bradfordizing can be applied to document types other than journal article, e.g. monographs (cf. Worthen 1975; Mayr 2008, 2009). Monographs e.g. provide ISBN numbers which are also good identifiers for the Bradfordizing analysis.

  3. 3.

    www.gesis.org/sowiport.

  4. 4.

    www.gesis.org/beta/prototypen/irm.

  5. 5.

    http://lucene.apache.org/solr/.

  6. 6.

    Actually, the author–author-relations are computed during indexing time and are retrieved by the system via particular facets added to the user’s query.

  7. 7.

    http://trec.nist.gov/.

  8. 8.

    http://www.clef-campaign.org/.

  9. 9.

    http://www.gesis.org/solis.

  10. 10.

    However, a retrieval study with experts from different domains is currently carried out.

  11. 11.

    Moreover, we observed a high range of re-rankings done by Author Centrality. More than 90% of the documents in the result sets were captured by the author centrality based ranking.

  12. 12.

    See Huberman and Adamic (2004) and Mutschke (2004b) for first attempts in that direction.

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Acknowledgments

We would like to express our grateful thanks to Andrea Scharnhorst for her valuable comments. Special thanks go to the students in two independent LIS courses at Humboldt University (guided by our former colleague Vivien Petras) and University of Applied Science in Darmstadt. These students took part in our first IRM retrieval test in the winter semester 2009/2010. We thank Hasan Bas who did the main implementation work for our assessment tool. The project is funded by DFG, grant No. INST 658/6-1.

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Correspondence to Peter Mutschke.

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Mutschke, P., Mayr, P., Schaer, P. et al. Science models as value-added services for scholarly information systems. Scientometrics 89, 349–364 (2011). https://doi.org/10.1007/s11192-011-0430-x

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Keywords

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