DocumentCode :
243626
Title :
Exploiting Paper Contents and Citation Links to Identify and Characterise Specialisations
Author :
Han Xu ; Martin, Eric ; Mahidadia, Ashesh
Author_Institution :
Sch. of Comput. Sci. & Eng., UNSW Sydney, Sydney, NSW, Australia
fYear :
2014
fDate :
14-14 Dec. 2014
Firstpage :
613
Lastpage :
620
Abstract :
A scientific domain consists of subfields that can be further refined into specialisations. Specialisations emerge, evolve and consolidate, as reflected in particular in literature development, along a contents-based dimension where important problems are stated and addressed, and along a communal dimension where researchers collaborate and compete to solve those problems. We propose a generic framework that aims at effectively identifying and characterising the main specialisations of the subfields of a scientific domain by leveraging both paper contents and citation links. More specifically, the latent knowledge structure of a domain is discovered and progressively refined along both the contents-based and communal dimensions. Qualitative and quantitative experimental results show that our method can identify fine-grained specialisation of subfields and characterise them with key attributes (keywords, key papers and key authors), providing insights that are beyond the resolution limit of non-specialised approaches. One of the direct benefits of this research is to fulfil the highly specialised information needs of a scholarly researcher and significantly facilitate literature exploration.
Keywords :
citation analysis; information needs; citation links; information needs; literature exploration; paper contents; scientific domain subfield specialisations; Analytical models; Communities; Data models; Measurement; Semantics; Speech recognition; Tagging; Contents and citation links; Multi-step community detection; Resolution limit; Specialisation characterisation; Specialisation discovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
Type :
conf
DOI :
10.1109/ICDMW.2014.26
Filename :
7022653
Link To Document :
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