Articolo in rivista, 2020, ENG, 10.1108/DTA-09-2019-0163
Manghi P.; Atzori C.; De Bonis M.; Bardi A.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
Purpose: Several online services offer functionalities to access information from "big research graphs" (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts. Design/methodology/approach: This work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments. Findings: GDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph. Originality/value: To our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.
Data technologies and applications 54 , pp. 409–435
deduplication, information graphs, big data, scholarly communication, scalability, implementation
De Bonis Michele, Manghi Paolo, Bardi Alessia, Atzori Claudio
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 432254
Year: 2020
Type: Articolo in rivista
Creation: 2020-09-25 17:23:42.000
Last update: 2021-03-11 14:02:45.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
URL: https://www.emerald.com/insight/content/doi/10.1108/DTA-09-2019-0163/full/html
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:432254
DOI: 10.1108/DTA-09-2019-0163
Scopus: 2-s2.0-85087022746
ISI Web of Science (WOS): 000546077000001