Entity deduplication in big data graphs for scholarly communication

Author:

Manghi PaoloORCID,Atzori ClaudioORCID,De Bonis Michele,Bardi AlessiaORCID

Abstract

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.

Publisher

Emerald

Subject

Library and Information Sciences,Information Systems

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Disambiguate Entity Matching using Large Language Models through Relation Discovery;Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI;2024-06-09

2. A Graph Neural Network Approach for Evaluating Correctness of Groups of Duplicates;Linking Theory and Practice of Digital Libraries;2023

3. Measuring the Concept of PID Literacy: User Perceptions and Understanding of PIDs in Support of Open Scholarly Infrastructure;Open Information Science;2023-01-01

4. Decision Support System using Weighting Similarity Model for Constructing Ground-Truth Dataset;2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI);2022-10-06

5. FDup: a framework for general-purpose and efficient entity deduplication of record collections;PeerJ Computer Science;2022-09-06

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