Semantic e-learning platform for chemistry
In cooperation with FIZ CHEMIE we develop a semantic e-learning platform in the domain of chemistry which is based on ChemgaPedia. ChemgaPedia is a multimedia, webbased eLearning service platform that currently contains about 18.000 pages organized in 1.700 chapters covering the complete bachelor studies in chemistry and related topics of chemistry, pharmacy and life sciences. The eLearning encyclopedia contains some 25.000 media objects and the eLearning platform provides services such as virtual and remote labs for experiments. With up to 350.000 users per month the platform is the most frequently used scientific educational service in the German spoken Internet.
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.
The aim of the cooperation between FIZ CHEMIE as the provider of ChemgaPedia and the Corporate Semantic Web expert group 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 path 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 path automatically. In order to represent the data in a semantic format we have built converters that parse the XML files and convert them to RDF. The converters identify chemical entities in the source content and match them to ontology concepts. Moreover, information relevant elearning, e.g., study time, target audience, or recommended readings, is extracted as well and represented as RDF.
To reuse existing ontologies from the chemical and life-science domains being under intense development by the scientific community, we have aligned our ontological model with ontologies such as the ChEBI ontology and the PubChem schemata from Bio2RDF. This allows us to enrich the chemical data described in ChemgaPedia with chemical identifiers and classifications from these ontologies and also to improve the search functionality by adding new and more precise data. Another important step is the alignment with DBpedia Germany that allows for using the elaborate SKOS based classification to enhance the learning material from ChemgaPedia with related content from DBpedia.