Cluster-based polyrepresentation as science modelling approach for information retrieval
Abstract
The increasing number of publications make searching and accessing the produced literature a challenging task. A recent development in bibliographic databases is to use advanced information retrieval techniques in combination with bibliographic means like citations. In this work we will present an approach that combines a cognitive information retrieval framework based on the principle of polyrepresentation with document clustering to enable the user to explore a collection more interactively than by just examining a ranked result list. Our approach uses information need representations as well as different document representations including citations. To evaluate our ideas we employ a simulated user strategy utilising a cluster ranking approach. We report on the possible effectiveness of our approach and on several strategies how users can achieve a higher search effectiveness through cluster browsing. Our results confirm that our proposed polyrepresentative cluster browsing strategy can in principle significantly improve the search effectiveness. However, further evaluations including a more refined user simulation are needed.Citation
Abbassi, M.K. and Frommholz, I. (2015) 'Cluster-based polyrepresentation as science modelling approach for information retrieval'. Scientometrics 102 (3) pp2301-2322.Publisher
Springer VerlagJournal
ScientometricsAdditional Links
http://link.springer.com/article/10.1007/s11192-014-1478-1Type
ArticleLanguage
enISSN
0138-9130ae974a485f413a2113503eed53cd6c53
10.1007/s11192-014-1478-1