Towards Controlled Natural Language for Semantic Annotation

Abstract

Richly interlinked metadata constitute the foundation of the Semantic Web. Manual semantic annotation is a labor intensive task requiring training in formal ontological descriptions for the otherwise non-expert user. Although automatic annotation tools attempt to ease this knowledge acquisition barrier, their development often requires access to specialists in Natural Language Processing (NLP). This challenges researchers to develop user-friendly annotation environments. Controlled Natural Languages (CNLs) offer an incentive to the novice user to annotate, while simultaneously authoring his/her respective documents in a user-friendly manner. CNLs have been successfully applied to ontology authoring, but little research has focused on their application to semantic annotation. This paper describes two novel approaches to semantic annotation, which permit non-expert users to simultaneously author and annotate meeting minutes using CNL. Finally, this work provides empirical evidence that for certain scenarios applying CNLs for semantic annotation can be more user friendly than a standard manual semantic annotation tool.

Capture and visualisation of text understanding through semantic annotations and semantic networks for teaching and learning

“…In general, automatic and semi-automatic annotation tools are justified because manually making annotations can be a labour-intensive and tedious task [11]. Moreover, manually made annotations are considered of limited value due to little consistency and uncontrolled quality [12]. However, the manual annotation can be useful when the automatic annotation is not possible, to get training data for automatic annotation tools based on machine learning, and to grasp insights about human annotators in applications such as teaching and learning.…”