environment in the German spoken chemical and pharmaceutical community. Up to 350.000 students, teachers, and trainees rely on the continuing availability of platform and content. The content within the platform consists of text and multimedia elements, such as animations, applications and videos. Teaching paths and the provision of pages and units are statically created by domain experts. The authors are responsible for producing content and relating chapters and pages.

![0_image_0.png](0_image_0.png)

The content is represented in XML format conforming to a schema specialized to represent eLearning content. All pages and media elements are stored in an XML-based database. A processing pipeline consisting mainly of XSLT transformations is used to convert the content to static HTML pages.

Up to now there is no method to generate dynamic paths or to assemble personalized content pages on demand.

## Strategy

The aim of the cooperation between FIZ CHEMIE as the provider of ChemgaPedia and the Corporate Semantic Web expert group providing know-how about semantic technologies and applications is the development of an ontology-based copy of the current ChemgaPedia platform.

For this purpose, we are currently developing two ontologies: a chemistry ontology and an eLearning ontology. In contrast to existing chemistry ontologies which are built for expert use, we use the expertise of the authors of ChemgaPedia to develop an ontology that is suitable for the use in the eLearning platform. For example, it also includes concepts that are not contained in existing ontologies but are relevant for students or it contains relationships that help to generate teaching paths automatically. Nevertheless, our goals are also to reuse existing ontologies as far as possible and to link to them in the sense of linked data.

The eLearning ontology models concepts and relationships to describe the content, relationships between lectures, and metadata about a lecture, e.g., target group.

In a first step towards an ontology-based eLearning platform we currently construct the needed ontologies and develop a demonstrator illustrating the usage of background knowledge to generate teaching paths automatically. In order to Fig. 4: Screenshot of the demonstration version

![0_image_1.png](0_image_1.png)

