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 word-embeddings. 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 relationships.

A Tool for Researchers: Querying Big Scholarly Data Through Graph Databases / Mercorio, F.; Mezzanzanica, M.; Moscato, V.; Picariello, A.; Sperlì, Giancarlo. - (2019). (Intervento presentato al convegno Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019 tenutosi a 20/09/2019 nel 16/09/2019).

A Tool for Researchers: Querying Big Scholarly Data Through Graph Databases

V. Moscato;A. Picariello;Giancarlo Sperlì
2019

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 word-embeddings. 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 relationships.
2019
978-3-030-46132-4
A Tool for Researchers: Querying Big Scholarly Data Through Graph Databases / Mercorio, F.; Mezzanzanica, M.; Moscato, V.; Picariello, A.; Sperlì, Giancarlo. - (2019). (Intervento presentato al convegno Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019 tenutosi a 20/09/2019 nel 16/09/2019).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/889664
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