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.

This chapter presents how an ontology network can be used to explicitly specify the relevant features of Semantic Educational Recommender Systems. This ontology network conceptualizes the different domains and features involved in these kind of systems in a set of interrelated ontologies. Basically, this chapter presents a detailed study of the semantic relationships that exist among the ontologies in the network considering learners and educators goals and taking also into account relevance feedback by users. One important contribution of this work is to show how the ontology-based reasoning mechanism can be used to validate the recommendation criteria and to assure flexibility for tailoring the educational resource adequacy features (called Educational Resource Quality).

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a b s t r a c tThis work is motivated by a real-world case study where it is necessary to integrate and relate existing ontologies through meta-modelling. For this, we introduce the Description Logic SH IQM which is obtained from SH IQ by adding statements that equate individuals to concepts in a knowledge base.In this new extension, concepts can be individuals of another concept (called meta-concept) which itself can be an individual of yet another concept (called meta-meta-concept) and so on. We define an algorithm that checks consistency of SH IQM by modifying the Tableau algorithm for SH IQ. From the practical point of view, this has the advantage that we can reuse the code of existing OWL reasoners. From the theoretical point of view, it has a similar advantage of reuse. We make use of the existing results and proofs that lead to correctness of the algorithm for SH IQ in order to prove correctness of the algorithm for SH IQM.

PurposeWeb site recommendation systems help to get high quality information. The modelling of recommendation systems involves the combination of many features: metrics of quality, quality criteria, recommendation criteria, user profile, and specific domain concepts, among others. At the moment of the specification of a recommendation system it must be guaranteed a right interrelation of all of these features. The purpose of this paper is to model a web site quality‐based recommendation system by an ontology network.Design/methodology/approachIn this paper, the authors propose an ontology network based process for web site recommendation modelling. The ontology network conceptualizes the different domains (web site domain, quality assurance domain, user context domain, recommendation criteria domain, specific domain) in a set of interrelated ontologies. Particularly, this approach is illustrated for the health domain.FindingsBasically, this work introduces the semantic relationships that were used to construct this ontology network. Moreover, it shows the usefulness of this ontology network for the detection of possible inconsistencies when specifying recommendation criteria.Originality/valueRecommendation systems based on ontologies that model the user profile and the domain of resources to be recommended are quite common. However, it is uncommon to find models that explicitly represent the criteria used by the recommender systems, that express the quality dimensions of resources and on which criteria are applied, and consider the user context at the moment of the query.