This demonstrator is designed to be run from a desktop or a laptop computer.

If you are using a phone or a tablet, some functionalities may not be displayed or not work properly.

Scientific mediation of the GREYC laboratory
Illustration of the demonstrator AnnoTag

AnnoTag

Team SAFE

Digital libraries build on classifying contents by capturing their semantics and (optionally) aligning the description with an underlying categorization scheme. This process is usually based on human intervention, either by the content creator or a curator. As such, this procedure is highly time-consuming and - thus – expensive. In order to support the human in data curation, we introduce an annotation tagging system called AnnoTag. AnnoTag aims at providing concise content annotations by employing entity-level analytics in order to derive semantic descriptions in the form of tags. In particular, we are generating "Semantic LOD Tags" (linked open data) that allow an interlinking of the derived tags with the LOD cloud. Based on a qualitative evaluation on Web news articles we prove the viability of our approach and the high-quality of the automatically extracted information.

You may also like