2.50
Hdl Handle:
http://hdl.handle.net/10547/333147
Title:
Cluster-based polyrepresentation as science modelling approach for information retrieval
Authors:
Abbasi, Muhammad Kamran; Frommholz, Ingo ( 0000-0002-5622-5132 )
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 Verlag
Journal:
Scientometrics
Issue Date:
2015
URI:
http://hdl.handle.net/10547/333147
DOI:
10.1007/s11192-014-1478-1
Additional Links:
http://link.springer.com/article/10.1007/s11192-014-1478-1
Type:
Article
Language:
en
ISSN:
0138-9130
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorAbbasi, Muhammad Kamranen
dc.contributor.authorFrommholz, Ingoen
dc.date.accessioned2014-10-24T11:34:46Zen
dc.date.available2014-10-24T11:34:46Zen
dc.date.issued2015en
dc.identifier.citationAbbassi, M.K. and Frommholz, I. (2015) 'Cluster-based polyrepresentation as science modelling approach for information retrieval'. Scientometrics 102 (3) pp2301-2322.en
dc.identifier.issn0138-9130en
dc.identifier.doi10.1007/s11192-014-1478-1en
dc.identifier.urihttp://hdl.handle.net/10547/333147en
dc.description.abstractThe 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.en
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.relation.urlhttp://link.springer.com/article/10.1007/s11192-014-1478-1en
dc.subjectpolyrepresentationen
dc.subjectdocument clusteringen
dc.subjectinformation retrievalen
dc.subjectbibliometricsen
dc.subjectsimulated useren
dc.titleCluster-based polyrepresentation as science modelling approach for information retrievalen
dc.typeArticleen
dc.identifier.journalScientometricsen
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