An annotation system as an abstraction layer to support collaborative knowledge building

In this poster, we present an annotation system as an abstraction layer to enrich the collaborative knowledge creation and curation experiences by structuring data extracted from the exchanges between users, between users and AI services, and from users' input on content. It supports the definition of more meaningful relations between concepts and richer discussion processes among users, contributing to the expansion and evolution of knowledge bases that feed off the aforementioned structured data. It is also capable of yielding relevant results to semantic queries by which users can retrieve content and knowledge they contributed to creating. Our results show that users found this method of joint knowledge building to be useful and that it could optimize tasks, mainly because a) it allows access to fresh insights, correlations, and valuable knowledge exchange, and b) it supports data retrieval via semantic queries.