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Today, several online services offer functionalities to access information from big scholarly communication graphs, which interlink entities such as publications, authors, datasets, organizations, etc. Such graphs are often populated over time as aggregations of multiple sources and therefore suffer from entity duplication problems. Although deduplication of graphs is a known and actual problem, solutions tend to be dedicated and address a few of the underlying challenges. In this paper, we propose the GDup system, an integrated, scalable, generalpurpose system for entity deduplication over big information graphs. GDup supports practitioners with the functionalities needed to realize a fully-fledged entity deduplication workflow over a generic input graph, inclusive of Ground Truth support, end-user feedback, and strategies for identifying and merging duplicates to obtain an output disambiguated graph. GDup is today one of the core components of the OpenAIRE infrastructure production system, monitoring Open Science trends on behalf of the European Commission.

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PurposeSeveral 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/approachThis 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.FindingsGDup 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/valueTo 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.