Annotation systems provide services that vary from adding simple Information for signifying content of Interest, to Indicate patterns In documents for creating statistical models and applying machine learning techniques. In this paper, we argue that AI mechanisms should be part of the annotation process to collaborate with annotation systems' end users (I.e. annotators) as oppose to be just an outcome from annotators' work. Indeed, current systems do not delve Into AI aspects to support the annotation process, lacking features that we argue as essential In annotation systems. That Is, collaboration between annotators and AI, collaborative knowledge curatlon by extracting and structuring knowledge from annotations considering the context of annotation anchors (I.e. area that was selected to create an annotation and comprises the content of Interest). To Illustrate the gains of this approach, we present HAS, the Hyperknowledge Annotation System. HAS allows one to annotate multimedia content (e.g., text. Image, and video) and Its tight Integration with Al-based services enables the extraction of additional semantic Information from the annotated content. In our approach, both the annotation and the Information extracted from the content are structured using the hyperknowledge conceptual model, which promotes the use of the spatlotemporal query capabilities of this model for retrieving annotations based on semantic queries. We argue that Integrating Al-based services and using the hyperknowledge model for knowledge structuring leverage multimedia annotation systems, enabling the development of novel use cases.