Automated Metadata Annotation:What Is and Is Not Possible with Machine Learning

作     者：Mingfang Wu Hans Brandhorst Maria-Cristina Marinescu Joaquim More Lopez Margorie Hlava Joseph Busch 

作者机构：Australian Research Data CommonsAustralian Research Data CommonsMelbourneAustraliaAustralia IconclassVoorscotenThe Netherlands Barcelona Supercomputing CenterBarcelonaSpain Access InnovationsAlbuquerqueNew MexicoUSA Taxonomy StrategiesWashingtonDCUSA 

出 版 物：《Data Intelligence》 (数据智能（英文）)

年 卷 期：2023年第5卷第1期

页      面：122-138页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 12[管理学] 120501[管理学-图书馆学] 120502[管理学-情报学] 

主　　题：Metadata annotation Metadata Machine learning Culture heritage Research data 

摘      要：Automated metadata annotation is only as good as training dataset,or rules that are available for the *** s important to learn what type of data content a pre-trained machine learning algorithm has been trained on to understand its limitations and potential *** what type of content is readily available to train an algorithm-what s popular and what s ***,scholarly and historical content is often not available in consumable,homogenized,and interoperable formats at the large volume that is required for machine *** are exceptions such as science and medicine,where large,well documented collections are *** paper presents the current state of automated metadata annotation in cultural heritage and research data,discusses challenges identified from use cases,and proposes solutions.