Exploratory search of academic publication and citation data using interactive tag cloud visualizations.

Autor/inn/en	Dunaiski, Marcel; Greene, Gillian J.; Fischer, Bernd
Titel	Exploratory search of academic publication and citation data using interactive tag cloud visualizations.
Quelle	In: Scientometrics, (2017) 3, S.1539-1571 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-016-2236-3
Schlagwörter	Tag clouds; Exploratory search; Formal concept analysis; Automatic keyphrase extraction; Citation analysis; User study
Abstract	Abstract Acquiring an overview of an unfamiliar discipline and exploring relevant papers and journals is often a laborious task for researchers. In this paper we show how exploratory search can be supported on a large collection of academic papers to allow users to answer complex scientometric questions which traditional retrieval approaches do not support optimally. We use our ConceptCloud browser, which makes use of a combination of concept lattices and tag clouds, to visually present academic publication data (specifically, the ACM Digital Library) in a browsable format that facilitates exploratory search. We augment this dataset with semantic categories, obtained through automatic keyphrase extraction from papers’ titles and abstracts, in order to provide the user with uniform keyphrases of the underlying data collection. We use the citations and references of papers to provide additional mechanisms for exploring relevant research by presenting aggregated reference and citation data not only for a single paper but also across topics, authors and journals, which is novel in our approach. We conduct a user study to evaluate our approach in which we asked 34 participants, from different academic backgrounds with varying degrees of research experience, to answer a variety of scientometric questions using our ConceptCloud browser. Participants were able to answer complex scientometric questions using our ConceptCloud browser with a mean correctness of 73%, with the user’s prior research experience having no statistically significant effect on the results.
Erfasst von	OLC
Update	2023/2/05