Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semi-automatic learning methods can be used to support the expert. We propose diagnostic scores as a promising approach and present a method for inductive learning of diagnostic scores. It can be be refined incrementally by applying differ...

Collaboration scripts can help learners to engage in argumentation and knowledge acquisition. However, they might have differential effects for learners holding differently structured knowledge (internal scripts) on argumentation. We investigated how external scripts interact with learners’ internal scripts concerning collaborative argumentation. 98 students from two secondary schools participa...

Taking advantage of the continuously improving, web-based learning systems plays an important role for self-learning, especially in the case of working people. Nevertheless, learning systems do not generally adapt to learners’ profiles. Learners have to spend a lot of time before reaching the learning goal that is compatible with their knowledge background. To overcome such difficulties, an e-l...

This paper offers a holistic and psychologically oriented perspective on health recommender systems and introduces a tool— CHUSAPEDIA—that is designed to support researchers and practitioners in putting this perspective into practice. One component of CHUSAPEDIA is a semantic wiki in which a wide range of knowledge is formalized about how people make choices and how these choices can be support...

AlvisAE is a text annotation editor aimed at knowledge acquisition projects. An expressive annotation data model allows AlvisAE to support various knowledge acquisition tasks like construction gold standard corpus, ontology population and assisted reading. Collaboration is achieved through a workflow of tasks that emulates common practices (e.g. automatic pre-annotation, adjudication). It is im...