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Science models for search: a study on combining scholarly information retrieval and scientometrics

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Abstract

Models of science address statistical properties and mechanisms of science. From the perspective of scholarly information retrieval (IR) science models may provide some potential to improve retrieval quality when operationalized as specific search strategies or used for rankings. From the science modeling perspective, on the other hand, scholarly IR can play the role of a validation model of science models. The paper studies the applicability and usefulness of two particular science models for re-ranking search results (Bradfordizing and author centrality). The paper provides a preliminary evaluation study that demonstrates the benefits of using science model driven ranking techniques, but also how different the quality of search results can be if different conceptualizations of science are used for ranking.

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Notes

  1. http://www.gesis.org/en/events/events-archive/conferences/issiworkshop2013/.

  2. http://www.gesis.org/en/events/events-archive/conferences/ecirworkshop2014/.

  3. Instead of addressing just local features such as citation counts of papers or author productivity.

  4. http://www.gesis.org/en/research/external-funding-projects/archive/irm/.

  5. ISSNs are stable identifiers for journals.

  6. Actually, co-authorships are computed during indexing time in advance and are retrieved by the system via particular facets added to the user’s query.

  7. To reduce computation effort pure single-authors are not added to the graph.

  8. http://sowiport.gesis.org/. Furthermore, sowiport provides simple re-ranking models such as citation counts of papers and author productivity as well.

  9. There are three extreme cases of very low precision values which need some explanation: A reasonable explanation for the low precision of SOLR in the case of topic 166 is most likely the low selectivity of the search term ‘Deutschland’ (Germany) in SOLIS which might negatively affect the precision of TF-IDF. A possible explanation for the low precision of BRAD in this case of topic 83 is the lower coverage of media related journals in SOLIS. Likewise, the low precision of AUTH in the case of topic 88 can be explained by the rather fragmentary representation of historical science topics in SOLIS which lead to less representative networks. However, much more detailed research needs to be done here.

  10. This issue is addressed by the COST action KNOWeSCAPE (http://knowescape.org/).

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Acknowledgments

We thank Philipp Schaer and Thomas Lüke who were the main developers and our co-investigators in the IRM projects. This work was funded by DFG, Grant No. INST 658/6-1 and SU 647/5-2.

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  1. GESIS–Leibniz-Institute for the Social Sciences, Unter Sachsenhausen 6-8, 50667, Cologne, Germany

    Peter Mutschke & Philipp Mayr

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Correspondence to Peter Mutschke.

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Mutschke, P., Mayr, P. Science models for search: a study on combining scholarly information retrieval and scientometrics. Scientometrics 102, 2323–2345 (2015). https://doi.org/10.1007/s11192-014-1485-2

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