Authors:
Navid Nobani
1
;
2
;
Mauro Pelucchi
3
;
Matteo Perico
4
;
Andrea Scrivanti
3
and
Alessandro Vaccarino
3
Affiliations:
1
Dept. of Informatics, Systems & Communication, University of Milan-Bicocca, Milan, Italy
;
2
Digital Attitude, Milan, Italy
;
3
CRISP Research Center, University of Milan-Bicocca, Milan, Italy
;
4
Oròbix, Bergamo, Italy
Keyword(s):
Graph Networks, Scientific Documents, Information Retrieval, Literature Review.
Abstract:
Over the past two decades, academia has witnessed numerous tools and search engines which facilitate the retrieval procedure in the literature review process and aid researchers to review the literature with more ease and accuracy. These tools mostly work based on a simple textual input which supposedly encapsulates the primary keywords in the desired research areas. Such tools mainly suffer from the following shortcomings: (i) they rely on textual search queries that are expected to reflect all the desired keywords and concepts, and (ii) shallow results which makes following a paper through time via citations a cumbersome task. In this paper, we introduce GRASP, a search engine that retrieves scientific papers starting from a sub-graph query provided by the user, offering (i) a list of time papers based on the query and (ii) a graph with papers and authors as vertices and edges being cited and published-by. GRASPhas been created using a Neo4j graph database, based on DBLP and AMiner
corpora provided by their API. Acting performance evaluation by asking ten computer science experts, we demonstrate how GRASPcan efficiently retrieve and rank the most related papers based on the user’s input.
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