Entity deduplication in big data graphs for scholarly communication

• Published in: Data technologies and applications Vol. 54; no. 4; pp. 409 - 435
• Main Authors: Manghi, Paolo, Atzori, Claudio, De Bonis, Michele, Bardi, Alessia
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 07.09.2020; Emerald Group Publishing Limited
• Subjects: Account aggregation; Algorithms; Authors; Big Data; Communication (Thought Transfer); Computer architecture; Computer Software; Data models; Datasets; Directories; Duplication; Feedback; Funding; Graphs; Ground truth; Identification; Infrastructure; Library Services; Metadata; Open access; Reproduction (copying); Researchers; Scholarly communication; Science; Scientific papers; Software; Topology; Trends; Workflow; Europe; Deduplication; Scholarly communication; Big data; Information graphs; Scalability; Implementation
• ISSN: 2514-9288; 2514-9318
• DOI: 10.1108/DTA-09-2019-0163

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.