Document - GDup: De-duplication of Scholarly Communication Big Graphs

2018

Conference article Open Access

GDup: De-duplication of Scholarly Communication Big Graphs

Atzori C., Manghi P., Bardi A.

information graphs scholarly communication deduplication big data

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, general-purpose 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.

Source: 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), pp. 142–151, Zurigo, 17-20/12/2018

Publisher: IEEE, New York, USA

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:401241,
	title = {GDup: De-duplication of Scholarly Communication Big Graphs},
	author = {Atzori C. and Manghi P. and Bardi A.},
	publisher = {IEEE, New York, USA},
	doi = {10.1109/bdcat.2018.00025},
	booktitle = {2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), pp. 142–151, Zurigo, 17-20/12/2018},
	year = {2018}
}