Abstract

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This works is motivated by a real-world case study where it is necessary to integrate and relate existing ontologies through metamodelling. For this, we introduce the Description Logic ALCQM which is obtained from ALCQ by adding statements that equate individuals to concepts in a knowledge base. In this new extension, a concept can be an individual of another concept (called meta-concept) which themselves can be individuals of yet another concept (called meta meta-concept) and so on. We define a tableau algorithm for checking consistency of an ontology in ALCQM and prove its correctness.

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Purpose -The broader adoption of the internet along with web-based systems has defined a new way of exchanging information. That advance added by the multiplication of mobile devices has required systems to be even more flexible and personalized. Maybe because of that, the traditional teaching-controlled learning style has given up space to a new way of learning, which is more flexible and adequate to the learners needs. The purpose of this research is to go further into the semantic modeling of adaptive web based learning systems. Particularly, the paper focuses on those learning systems that consider in their definition the awareness of student's context in order to properly react to the student needs. Design/methodology/approach -In this paper the authors introduce a semantic model of the student context in terms of an ontology network. This semantic model is explored in order to detect the "current situation" of students when they are navigating into e-learning environments. The final objective is to enrich the adaptation functionality of e-learning environments, being able to evaluate context data from personal profile, learning domain and technological situation. Findings -In order to evaluate the semantic model defined, examples of detected situations are shown in accordance to specific e-learning scenarios. Originality/value -The paper covers definition of a flexible and modularized model by using ontology networks, which can be easily modified to incorporate new knowledge data, aiding the modeling of concepts from different learning environments.