Functionalities for automatic metadata generation applications: a survey of metadata experts' opinions

TLDR

The study surveys metadata experts on the AMeGA project’s automatic metadata generation applications. The authors review automatic metadata generation research and detail their survey methods, key findings, and conclusions. Experts expect higher accuracy for technical metadata with automation, support its implementation, but prefer retaining human evaluation for intellectually demanding metadata; these insights inform Version 1.0 of recommended functionalities.

Abstract

This paper reports on the automatic metadata generation applications (AMeGA) project's metadata expert survey. Automatic metadata generation research is reviewed and the study's methods, key findings and conclusions are presented. Participants anticipate greater accuracy with automatic techniques for technical metadata (e.g., ID, language, and format metadata) compared to metadata requiring intellectual discretion (e.g., subject and description metadata). Support for implementing automatic techniques paralleled anticipated accuracy results. Metadata experts are in favour of using automatic techniques, although they are generally not in favour of eliminating human evaluation or production for the more intellectually demanding metadata. Results are incorporated into Version 1.0 of the Recommended Functionalities for automatic metadata generation applications (Appendix A).