Metadata Extraction and Harvesting

TLDR

The study examines the capabilities of two Dublin Core automatic metadata generation tools, Klarity and DC-dot. The authors submitted the top‑level web pages of 29 NIEHS resources to both generators to evaluate their performance. The results show that extraction algorithms and harvesting of human‑created META tags both enhance automatic metadata generation, and that combining these approaches yields optimal metadata, though further research is needed to determine when each method should be applied.

Abstract

Abstract This research explores the capabilities of two Dublin Core automatic metadata generation applications, Klarity and DC-dot. The top level Web page for each resource, from a sample of 29 resources obtained from National Institute of Environmental Health Sciences (NIEHS), was submitted to both generators. Results indicate that extraction processing algorithms can contribute to useful automatic metadata generation. Results also indicate that harvesting metadata from META tags created by humans can have a positive impact on automatic metadata generation. The study identifies several ways in which automatic metadata generation applications can be improved and highlights several important areas of research. The conclusion is that integrating extraction of harvesting methods will be the best approach to creating optimal metadata, and more research is needed to identify when to apply which method.