A Workflow-Based Methodological Framework for Hybrid Human-AI Enabled Scientometrics

Correia, António; Jameel, Shoaib; Schneider, Daniel; Paredes, Hugo; Fonseca, Benjamim

doi:10.1109/bigdata50022.2020.9378096

Cited by 4 publications

(2 citation statements)

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“…On the other hand, supervised models require pre-labeled data to train ML algorithms able to group similar entities based on clustering techniques. Concurrently, the use of crowdsourcing in such tasks remained less investigated and has not been addressed at the crossroads of bibliometric-enhanced IR extensively [12]. Within this context of application, Cheng and co-authors [13] published one of the first known studies using discriminative feature labeling and crowdsourcing for author name disambiguation.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Regarding the difficult task of cleaning the raw data during the preprocessing stage of a scientometric workflow [12], we propose a system that directly addresses the inadequacies of name ambiguity by crowdsourcing disambiguation tasks. Thus, we assume that a crowd-based approach can offer stable performance for coping with data quality dimensions such as consistency, accuracy, completeness, and uniqueness [16].…”

Section: Authcrowd Systemmentioning

confidence: 99%

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AuthCrowd: Author Name Disambiguation and Entity Matching using Crowdsourcing

Correia

Guimarães

Paulino

et al. 2021

2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Despite decades of research and development in named entity resolution, dealing with name ambiguity is still a challenging issue for many bibliometric-enhanced information retrieval (IR) tasks. As new bibliographic datasets are created as a result of the upward growth of publication records worldwide, more problems arise when considering the effects of errors resulting from missing data fields, duplicate entities, misspellings, extra characters, etc. As these concerns tend to be of large-scale, both the general consistency and the quality of electronic data are largely affected. This paper presents an approach to handle these name ambiguity problems through the use of crowdsourcing as a complementary means to traditional unsupervised approaches. To this end, we present "AuthCrowd", a crowdsourcing system with the ability to decompose named entity disambiguation and entity matching tasks. Experimental results on a real-world dataset of publicly available papers published in peer-reviewed venues demonstrate the potential of our proposed approach for improving author name disambiguation. The findings further highlight the importance of adopting hybrid crowd-algorithm collaboration strategies, especially for handling complexity and quantifying bias when working with large amounts of data.

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Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Authcrowd Systemmentioning

confidence: 99%