Оntological approach to aggregated evaluation of the work of teams with multiple levels of hierarchy
Keywords:ontological approach, competence, hierarchical team, aggregated evaluation, military unit, semantic proximity, machine learning
Evaluation of organizations with multi-level hierarchical structure (project groups, groups of researchers, military units, expert commissions, medical rapid response teams) is an urgent problem today, the solution of which will allow to organize their rapid dynamic adaptation in a new way to perform important operational tasks, as well as training team members in the absence of sufficient competencies, skills and experience. The assessment also reveals the strengths and weaknesses of both the team as a whole and each participant, which in the future provides opportunities for their further growth. Evaluation of the work of teams is complex, including both professional knowledge in a certain field of activity and team management skills (so-called leadership skills, the ability to organize the execution of tasks and the achievement of goals and obtaining positive work results). In order to improve the evaluations of hierarchical aggregated teams we propose the use of ontological approach: domain ontology defines knowledge about relevant combinations of competencies for hierarchical team positions. Information about competencies of applicants is acquired by portfolio analysis (resume, certificates, diplomas, merits, etc.). More complex tasks can use a hierarchical set of ontologies and Web-services for obtaining evaluation results and recommendations for competence improving for various sublevels. We describe the step-by-step method of team evaluation that use elements of semantic similarity between different information objects for the matching of applicants and equipment with team positions. Proposed approach to hierarchical team evaluation is a component for integrated multi-criteria decision-making oriented on some special case of user tasks: the set of evaluation criteria is determined by task and can be built on the basis of domain knowledge, but importance of particular criterion is defined by state of environment in different points in time. This approach can be expanded by means and methods of knowledge management, Data mining and machine learning used for acquisition of competence knowledge.
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