An ontology modelling human resources management for innovational domains

Authors

  • Anatolii Hladun International research and training center of information technologies and systems of National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Kyiv, http://orcid.org/0000-0002-4133-8169
  • Yuliia Rohushyna Institute of software systems of National Academy of Sciences of Ukraine, Kyiv, http://orcid.org/0000-0001-7958-2557
  • Ihor Subach Institute of special communication and information protection of National technical university of Ukraine “Igor Sikorsky Kyiv polytechnic institute”, Kyiv,

DOI:

https://doi.org/10.20535/2411-1031.2018.6.1.153125

Keywords:

Competence, ontology, knowledge processing, innovation domain, human resources retrieval, human resources management, research activities, scientometric indicators

Abstract

We propose objective methods of the semantic comparison of the business task description with documents describing competencies of applicants. The advantages of qualifications describing through the triad of professional qualities – knowledge, skills and competencies – are demonstrated. Scientific novelty of this work deals with the use of atomic competencies as a main component of original ontological qualification model that becomes an instrument for solving of the complex scientific problem of qualification matching. The authors develop the original ontology that describe the high-level relations of employers and employees. This ontology can be integrated with domain and organizational ontologies that specialized these relations for some concrete task. Such approach can be useful for innovation domains (new or multidiscipline) where expert groups and formal specifications of professional skills stand on stage of formation. Information acquired from natural language documents that characterize applicants is represented by ontology-based thesauri that objectively reflect area of expertise of participants and thesaurus of project built by it’s natural language descriptions. Than these thesauri are matched on semantic level to define what part of such competencies is relevant to project needs. These methods provide the detection of the most relevant specialists able to carry out specific tasks of new promising domains. The retrieval can be provided in the Web-open environment or in natural language documents proposed by applicants.  The results of this research can be used for Human Resources Management in automated semantic evaluation of competencies for new and multidiscipline subject domains deal with scientific research and knowledge processing that evaluated by scientometric measures. Proposed methods uses domain knowledge and information about structure of research activities formalized by ontologies. These methods are based on semantic matching of description of the documents (diplomas, certificates, articles, monographs, conference materials, Web sites etc.), describing the competence of standard developers in chosen domain.

Author Biographies

Anatolii Hladun, International research and training center of information technologies and systems of National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Kyiv,

candidate of technical sciences,
associate professor, senior researcher,
head of the department

Yuliia Rohushyna, Institute of software systems of National Academy of Sciences of Ukraine, Kyiv,

candidate of physical and mathematical sciences,
associate professor, senior researcher

Ihor Subach, Institute of special communication and information protection of National technical university of Ukraine “Igor Sikorsky Kyiv polytechnic institute”, Kyiv,

doctor of technical science,
associate professor, head at the cybersecurity
and application of information systems
academic department

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Published

2018-07-01

How to Cite

Hladun, A., Rohushyna, Y., & Subach, I. (2018). An ontology modelling human resources management for innovational domains. Information Technology and Security, 6(1), 15–25. https://doi.org/10.20535/2411-1031.2018.6.1.153125

Issue

Section

INFORMATION TECHNOLOGY