AUTHOR=Lin Shaofu , Xu Zhe , Sheng Ying , Chen Lihong , Chen Jianhui 

TITLE=AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.739535

DOI=10.3389/fnins.2021.739535

ISSN=1662-453X

ABSTRACT=Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, existing studies mainly adopt experts or process recordings-based methods to obtain neuroimaging provenances and cannot meet the requirements of open research sharing in computational neuroscience. This paper proposes a literature mining based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging events containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLoS One. Experimental results show that the proposed method provides a practical approach to quickly collect research knowledge for neuroimaging provenance construction oriented to open research sharing.