Semantization of Agricultural Advisory Services for Validation of Outcomes of Non-Formal and Informal Learning. II

Abstract

Introduction. A characteristic feature of the modern agricultural sector is the use of advisory that provides the implementation of modern technologies into the production process. We analyze the specifics of existing advisory systems, their goals, main activities, and problems. This analysis causes expediency of documentation and validation of informal and non-formal outcomes of learning typical for agriculture and their processing by knowledge-oriented services based on modern Semantic Web technologies and resources. Purpose. This work is intended for the integration of labour and education markets and is aimed at semantization of agricultural advisory services for the expansion of advisory system functionality with the help of validation of outcomes of non-formal and informal learning of potential employees. Processing of semantics is based on the use of knowledge about agriculture subjects from internal and external ontologies by advisory intelligent applications. Such processing requires the creation of a relevant formal model that describes all main objects and subjects of agro-advisory activities, development of formalization methods for model components and defining of matching criteria based on internal and external ontologies. Software realization of proposed solution by AdvisOnt system is aimed at demonstration of its efficiency for practical agro-advisory tasks and advantages of semantic approach. Methods. In this work, we use methods of mathematical modeling, elements of ontological analysis and logical inference. Results. We propose an advisory system AdvisOnt that analyses the outcomes of non-formal and informal learning and ensures their validation for more efficient matching of information about potential employees, employers and agricultural educational resources. AdvisOnt is based on ontological representation of this knowledge formalized by competencies, vacancies, training courses, user profiles, etc. The system is aimed to generate recommendations for employment or further learning of necessary competencies by matching these objects. External knowledge bases are used for semantic formalization of vacancies and resumes for their more pertinent matching with the help of agricultural domain knowledge and competence classifications. AdvisOnt users receive recommendations on employment and about training courses that provide advisable competencies. Conclusion. Sustainable development of agro-industrial production needs rapid dissemination of agricultural knowledge and information, mobility and continuous training of agricultural professionals provided by advisory systems. We suggest how using ontological knowledge for advisory services allows to expand the possibilities of counseling. In the future, we plan to consider the ways of integration of AdvisOnt system that validates outcomes of informal and non-formal learning with other counseling and recommendation systems in the field of education and employment taking into account the specifics of the agricultural sector through external domain and organizational ontologies. The openness of the proposed solution is based on Semantic Web technologies and service-oriented programming.