Journal article  Open Access

Entity deduplication in big data graphs for scholarly communication

Manghi P., Atzori C., De Bonis M., Bardi A.

information graphs  scholarly communication  implementation  Library and Information Sciences  Information Systems  deduplication  big data  scalability 

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.

Source: Data technologies and applications 54 (2020): 409–435. doi:10.1108/DTA-09-2019-0163


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BibTeX entry
	title = {Entity deduplication in big data graphs for scholarly communication},
	author = {Manghi P. and Atzori C. and De Bonis M. and Bardi A.},
	doi = {10.1108/dta-09-2019-0163},
	journal = {Data technologies and applications},
	volume = {54},
	pages = {409–435},
	year = {2020}

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