- There is a boost in the interest on ontology with the developments in Semantic Web technologies. Ontologies play a vital role in semantic web. Even though there is lot of work done on ontology, still a standard framework for ontology engineering has not been defined. Even though current ontology engineering methodologies are available they need improvements. The effort of our work is to integrate various methods, techniques, tools and etc to different stages of proposed ontology engineering life cycle to create a comprehensive framework for ontology engineering. Current methodologies discuss ontology engineering stages and collaborative environments with user collaboration. However, discussion on increasing effectiveness and correct inference has been given less attention. More over, these methodologies provide little discussion on usability of domain ontologies. We consider these aspects as more important in our work. Also, ontology engineering has been done for various domains and for various purposes. Our effort is to propose an iterative and incremental approach for ontology engineering especially for e-learning domain with the intention of achieving a higher usability and effectiveness of e-learning systems. This paper introduces different aspects of the proposed ontology engineering framework and evaluation of it
Many experimental ontologies have been developed for the learning domain for use at different institutions. These ontologies include different OWL/OWL 2 (Web Ontology Language) constructors. However, it is not clear which OWL 2 constructors are the most appropriate ones for designing ontologies for the learning domain. It is possible that the constructors used in these learning domain ontologies match one of the three standard OWL 2 profiles (sublanguages). To investigate whether this is the case, we have analysed a corpus of 14 ontologies designed for the learning domain. We have also compared the constructors used in these ontologies with those of the OWL 2 RL profile, one of the OWL 2 standard profiles. The results of our analysis suggest that the OWL 2 constructors used in these ontologies do not exactly match the standard OWL 2 RL profile, but form a subset of that profile which we call OWL 2 Learn.
The terminology used by a particular educational institution is usually specific to that institution. Therefore, it is not possible to directly deploy a learning system that has been developed for one institution at another institution. Semantic interoperability among learning systems has been proposed as a solution to overcome terminological and structural differences. However, semantic interoperability is difficult to achieve in an educational environment since it requires a common ontology that is based on a shared understanding of the learning domain. To circumvent this problem, we introduce an adaptive learning system that relies on a domain-specific ontology that can be plugged into the learning system. In order to demonstrate the plug and play architecture of our system and its reasoning services, we compare two instances of the learning system we have created: one for Macquarie University and one for Charles Sturt University. We show how user queries can be answered over the domain-specific ontology at each institution using a description logic reasoner.
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