GRASP: Graph-based Mining of Scientific Papers

Navid Nobani, Navid Nobani, Mauro Pelucchi, Matteo Perico, Andrea Scrivanti, Alessandro Vaccarino

2021

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.

Paper Citation

in Harvard Style

Nobani N., Pelucchi M., Perico M., Scrivanti A. and Vaccarino A. (2021). GRASP: Graph-based Mining of Scientific Papers. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-521-0, pages 176-183. DOI: 10.5220/0010518901760183

in Bibtex Style

@conference{data21,
author={Navid Nobani and Mauro Pelucchi and Matteo Perico and Andrea Scrivanti and Alessandro Vaccarino},
title={GRASP: Graph-based Mining of Scientific Papers},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2021},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010518901760183},
isbn={978-989-758-521-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - GRASP: Graph-based Mining of Scientific Papers
SN - 978-989-758-521-0
AU - Nobani N.
AU - Pelucchi M.
AU - Perico M.
AU - Scrivanti A.
AU - Vaccarino A.
PY - 2021
SP - 176
EP - 183
DO - 10.5220/0010518901760183