Authors:
André Rattinger
1
;
Jean-Marie Le Goff
2
and
Christian Guetl
3
Affiliations:
1
IPT-DI, CERN, Espl. des Particules, Meyrin, Switzerland, Institute of Interactive Systems and Data Science, Graz University of Technology, Graz and Austria
;
2
IPT-DI, CERN, Espl. des Particules, Meyrin and Switzerland
;
3
Institute of Interactive Systems and Data Science, Graz University of Technology, Graz and Austria
Keyword(s):
Bibliometrics, Information Retrieval, Relevance Feedback, Visual Analytics, Patent Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
Visual Data Mining and Data Visualization
Abstract:
Collaboration Spotting is a knowledge discovery web platform that visualizes linked data as graphs. This platform enables users to perform operations to manipulate the graph to see and explore different facets of complex networks with multiple node and edge types. It combines information retrieval and graph analysis to effectively explore arbitrary data-sets. The platform is designed in a way that non-expert users without data science knowledge can explore it. For this, the data has to be specifically crafted in a form of a schema. The paper explores the platform in a bibliometrics context and demonstrates its search and relevance feedback mechanisms which can be applied through the navigation of an underlying knowledge graph based on publication and patent metadata. This demonstrates a novel way to interactively explore linked datasets through the combination of visual analytics for graphs with the combination of relevance feedback.