The economy is not keeping pace with the increasing speed of technological evolution. The inadequacy of the current system of education is a possible reason for this. Evolution forces us to produce experts for tasks and businesses which do not yet exist, to teach them technologies which have not yet been devised. The best way to produce experts is to accentuate the learner's best abilities and skills, assess the learner's potential and develop it further. We badly need revolutionary methods to facilitate intelligent personalization of study processes and approaches to make innovative education content more attractive and motivational for the learner. Advanced information management services and platforms play a valuable role in education process development, enabling new generations of students and education-related content providers to create, share, search, combine and deliver reliable and competent information. Earlier learner involvement in study content co-creation or personalization processes might dramatically increase student motivation and speed up the study process. Like any other products or services, e-Learning services need marketing to attract customers and make them a valuable source. To achieve a vision of ubiquitous knowledge, the next generation of innovative education environments will apply the achievements of the Open Data initiative and move towards learner-driven society-oriented systems. Therefore, this paper touches on different aspects of co-creative innovative education environment and correspondent e-Learning marketing strategies.

Expectations regarding the new generation of Web depend on the success of Semantic Web technology. Resource Description Framework (RDF) is a basis for explicit and machine-readable representation of semantics. However RDF is not suitable for describing dynamic and context-sensitive resources (eg. processes). We present the Context Description Framework (CDF) as an extension of the RDF by adding a 'TrueInContext' component to the basic RDF triple ('subject-predicate-object'), and consider contextual value as a container of RDF statements. We also add a probabilistic component, which allows multilevel contextual dependence descriptions as well as presumes possibility for Bayesian reasoning with the RDF model.

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Abstract:Agent-oriented approach has proven to be very efficient in engineering complex distributed software environments with dynamically changing conditions. The efficiency of underlying modelling framework for this domain is undoubtedly of a crucial importance. Currently, a model-driven architecture has been the most popular and developed for purposes of modelling different aspects of multi-agent systems, including behaviour of individual agents. UML is utilized as a basis for this modelling approach and variety of existing UML-based modelling tools after slight extension are reused. This paper proposes an ontology-driven approach to modelling agent behaviour as an emerging paradigm that originates from the Semantic Web wave. The proposed approach aims at modelling a proactive behaviour of (web-)resources through their representatives: software agents. In general, the presented research puts efforts into investigation of beneficial features of ontology-based agent modelling in comparison with conventional model-driven approaches.

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Abstract-Information representation plays very important role for services and applications to be more attractive for end user. One design cannot fit varied preferences and be suitable for all users. User driven customization of user interface applied for particular application or service do not influence at overall satisfaction of a user and usability of other applications and services. Therefore, in the paper authors tackle challenges of UI personalization on semantic level; and present a framework for adaptive UI development, driven by personal semantic user profile and mashup of reusable adaptive visualization modules. Considering human as a powerful integrated part of IoT environment, UI adaptation and personalization framework has been extended with automated ontology-based UI creation (Semantic Scanner concept). Presented approach of on-the-fly semantically-driven adaptive UI creation facilitates a process of human integration into machine-oriented infrastructure.

Personalized learning is increasingly gaining popularity, especially with the development of information technology and modern educational resources for learning. Each person is individual and has different knowledge background, different kind of memory, different learning speed. Teacher can adapt learning course, learning instructions or learning material according to the majority of learners in class, but that means that learning process is not adapted to the personality of each individual learner. That is why it is important to have smart educational process based on personal learning capabilities. This paper presents a literature survey on different learning systems which detects learning progress and based on that a model of smart educational system which use knowledge engineering and Watson technology is proposed. This system is relevant both for basic education and for adult education.

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