Paper Info
Title
Contextualization Of Topics - Browsing Through Terms, Authors, Journals And Cluster Allocations
Abstract
This paper builds on an innovative Information Retrieval tool, Ariadne. The tool has been developed as an interactive network visualization and browsing tool for large-scale bibliographic databases. It basically allows to gain insights into a topic by contextualizing a search query (Koopman et al., 2015). In this paper, we apply the Ariadne tool to a far smaller dataset of 111,616 documents in astronomy and astrophysics. Labeled as the Berlin dataset, this data have been used by several research teams to apply and later compare different clustering algorithms. The quest for this team effort is how to delineate topics. This paper contributes to this challenge in two different ways. First, we produce one of the different cluster solutions and second, we use Ariadne (the method behind it, and the interface - called LittleAriadne) to display cluster solutions of the different group members. By providing a tool that allows the visual inspection of the similarity of article clusters produced by different algorithms, we present a complementary approach to other possible means of comparison. More particularly, we discuss how we can - with LittleAriadne - browse through the network of topical terms, authors, journals and cluster solutions in the Berlin dataset and compare cluster solutions as well as see their context.
Year
Venue
Field
2015
PROCEEDINGS OF ISSI 2015 ISTANBUL: 15TH INTERNATIONAL SOCIETY OF SCIENTOMETRICS AND INFORMETRICS CONFERENCE
Data science,Graph drawing,Cluster (physics),Web search query,Data mining,Computer science,Cluster analysis,Contextualization
DocType
Volume
ISSN
Journal
abs/1504.04208
2175-1935
Citations 
PageRank 
References 
3
0.44
5
Authors
3
Name
Order
Citations
PageRank
Rob Koopman1454.37
Shenghui Wang230.44
Andrea Scharnhorst332841.63