Original scientific paper
Scholarly reference trees
Kristina Kocijan
; Faculty of Humanities and Social Sciences, University of Zagreb
Marko Požega
; Faculty of Humanities and Social Sciences, University of Zagreb
Dario Poljak
; Faculty of Humanities and Social Sciences, University of Zagreb
Abstract
In this paper, we propose, explain and implement bibliometric data analysis and visualization model in a web environment. We use NLP syntactic grammars for pattern recognition of references used in scholarly publications. The extracted information is used for visualizing author egocentric data via tree like structure. The ultimate goal of this work is to use the egocentric trees for comparisons of two authors and to build networks or forests of different trees depending on the forest’s attributes. We have stumbled upon many different problems ranging from exceptions in citation style structures to optimization of visualization model in order to achieve an optimal user experience. We will give a summary of our grammars’ restrictions and will provide some ideas for possible future work that could improve the overall user experience. The proposed trees can function by themselves, or they can be implemented in digital repositories of libraries and different types of citation databases.
Keywords
science mapping; scholarly data; network visualization; egocentric networks; NLP; information extraction; pattern recognition; reference styles; ReferenceTree
Hrčak ID:
176678
URI
Publication date:
6.3.2017.
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