Ontology-Based Models and Techniques in Multi-Subject Intellectual ISs for Cognitive Human-Machine Communications in Heterogeneous Environments
|Lead Author||Maxim, Shishaev|
|Institution Contact||Murmansk Arctic State University|
|Co-Authors||Alexander Vitsenty, Institute for Informatics KSC RAS, Russia Pavel Lomov, Murmansk Arctic State University, Russia Vladimir Dikovitsky, Institute for Informatics KSC RAS, Russia|
|Theme||Theme 1: Vulnerability of Arctic Environments|
|Session Name||1.8 Novel approaches to communicate research facts and predictions of the future of Arctic marine biota to non-scientific stakeholders|
|Datetime||Fri, Sep 16, 2016 11:00 AM - 11:15 AM|
|Abstract text||Arctic marine systems are the subject of the interest for different kinds of researchers. A lot of heterogeneous information should be presented for different users in appropriate manner to facilitate its perception and interpretation. Information system addressing such issue have to collect and store information in non-contradiction way. Another crucial problem is to select appropriate information to solve given task and to represent it in most efficient way. We call such a system a ‘multi-subject’ one (MSIS). For recent years we have developed a set of the models and techniques aimed to construct and control multi-subject information systems for different domains. Mentioned models and techniques are based on number of artificial intellect technologies in particular on the formal knowledge representation with ontologies:
• technique for ontologies creation and integration, based on common thesaurus and continuing feedback named ‘user as an expert’. This approach allows to exclude expensive and time-consuming efforts in searching the information;
• technology of cognitive interface synthesis that is grounded on the user’s mentality models which are automatically formed by continuously monitoring of user's activity;
• "semantic-space-time" data model allowing to form visual images of researched objects and phenomena, setting up the mapping data according to end-user needs, taking into account the characteristics of the subject area and perception of graphical information;
• special search technique based on extendable request, user’s mental models and subtractive relations;
• methods of the cognitive visualization of the Arctic marine biota.
By delegating part of human’s cognitive functions to machine and by taking into account users mental stereotypes within mentioned technologies we can improve the perception of the research facts, predictions and other specific information by different kinds of stakeholders.
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