We demonstrate GraphDBLP, a tool to allow researchers for querying the DBLP bibliography as a graph. The DBLP source data were enriched with semantic similarity relationships computed using wordembeddings. A user can interact with the system either via a Web-based GUI or using a shell-interface, both provided with three parametric and pre-defined queries. GraphDBLP would represent a first graph-database instance of the computer scientist network, that can be improved through new relationships and properties on nodes at any time, and this is the main purpose of the tool, that is freely available on Github. To date, GraphDBLP contains 5+ million nodes and 24+ million relationship.
Mercorio, F., Mezzanzanica, M., Moscato, V., Picariello, A., Sperlì, G. (2020). A Tool for Researchers: Querying Big Scholarly Data Through Graph Databases. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) (pp.760-763). Springer [10.1007/978-3-030-46133-1_46].
A Tool for Researchers: Querying Big Scholarly Data Through Graph Databases
Mercorio, F
;Mezzanzanica, M;
2020
Abstract
We demonstrate GraphDBLP, a tool to allow researchers for querying the DBLP bibliography as a graph. The DBLP source data were enriched with semantic similarity relationships computed using wordembeddings. A user can interact with the system either via a Web-based GUI or using a shell-interface, both provided with three parametric and pre-defined queries. GraphDBLP would represent a first graph-database instance of the computer scientist network, that can be improved through new relationships and properties on nodes at any time, and this is the main purpose of the tool, that is freely available on Github. To date, GraphDBLP contains 5+ million nodes and 24+ million relationship.
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