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Other types of metadata generation could be added to our workflows using other types of content.
HIVE supports dynamic subject metadata generation using multiple SKOS encoded controlled vocabularies.
Jeung, et al. [42] present an automated metadata generation approach that includes a probabilistic measure of data quality.
The results highlight the value of the HIVE approach for information management systems seeking to work with multiple vocabularies for metadata generation in an intelligent manner.
We claim that the Semantic Web, at its current stage of development, is in a state of a critical need of metadata generation and usage schemata that are specific, well-defined and easy to understand.
This approach stands upon two main processes: metadata generation and query resolution.
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We generate synthetic mobile video metadata using the generation algorithm method in [30] to evaluate the proposed GIFT.
The steps in the workflow include: Use case development; information modeling; semantic annotation; metadata registration; code generation; system deployment.
Most of the effort towards the generation of metadata has focused on who, what, when and where observations are made.
Instead, the generation of metadata is done by the end-users of the LOs, who can describe educational resources with tags that are meaningful to them and that can facilitate users' searching and retrieval of previously used and already known LOs (Doush, 2011; Huang et al. 2011).
The generation of metadata reports of this sort is not limited to schools.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com