An ontology modelling human resources management for innovational domains
Keywords:Competence, ontology, knowledge processing, innovation domain, human resources retrieval, human resources management, research activities, scientometric indicators
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.
Сabinet of Мinisters of Ukraine. (Nov. 23, 2011). The Resolution of the Сabinet of Мinisters no. 1341 “On Approval of the National Qualifications Framework”. [Online]. Available: http://zakon1.rada.gov.ua/laws/show/1341-2011-%D0%BF. Accessed on: March 15, 2018.
K. Legge, Human Resource Management: Rhetorics and Realities. London, UK: Red Globe Press, 2005.
C. Bizer, R. Heese, M. Mochol, R. Oldakowski, R. Tolksdorf, and R. Eckstein, “The Impact of Semantic Web Technologies on Job Recruitment Processes”; in Proc. 7th International Conference Wirtschaftsinformatik, Bamberg, 2005, pp. 1367-1381. doi: 10.1007/3-7908-1624-8_72.
E. Biesalski, and A. Abecker, “Human Resource Management with Ontologies”. In Biennial Conference on Professional Knowledge Management, Berlin, 2005, pp. 499-507. doi: 10.1007/11590019_57.
F. Trichet, M. Bourse, M. Leclere, and E. Morin, “Human resource management and semantic web technologies”, in Proc. Information and Communication Technologies: From Theory to Applications, 2004, pp. 641-642. doi:10.1109/ICTTA.2004.1307928.
P. Sparrow, C. Brewster, and C. Chung, Globalizing human resource management. London, UK: Routledge, 2016.
L. Razmerita, A. Angehrn, and A. Maedche, “Ontology-based user modeling for knowledge management systems”. in Proc. International Conference on User Modeling, Berlin, 2003, pp. 213-217. doi:10.1007/3-540-44963-9_29.
A. Gómez-Pérez, J. Ramírez, and B.Villazón-Terrazas, “An ontology for modelling human resources management based on standards”, in Proc. International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2007, Berlin,
pp. 534-541. doi: 10.1007/978-3-540-74819-9_66.
Yu. Rogushina, and A. Gladun, “Ontology-based competency analysis in new research domains”, Journal of Computing and Information Technology, vol. 23, no. 4, pp. 123-134, 2012. doi: 10.2498/cit.1002034.
Y. V. Granovsky, “Is it possible to measure science? V.V.Nalimov's research in scientometrics”, Scientometrics, vol. 52, iss. 2, pp.127-150, 2001. doi: 10.1023/A:1017991017982.
P. Vinkler, “Relations of relative scientometric impact indicators. The relative publication strategy index”, Scientometrics, vol. 40, iss. 1, pp. 163-169, 1997. doi: 10.1007/BF02459266.
J. E. Hirsch, “An index to quantify an individual’s scientific research output”, in Proc. of the National academy of Sciences of the United States of America, San Diego, 2005, vol. 85,
iss. 3, pp. 741-754. doi:10.1073/pnas.0507655102.
L. Egghe, “The Hirsch index and related impact measures”, Annual review of information science and technology, vol. 44, iss. 1, pp. 65-114, 2010. doi:10.1002/aris.2010.1440440109.
L. Bornmann, and H.-D. Daniel, “The state of h index research”, EMBO reports, vol. 10, iss.1, pp. 2-6, 2009. doi: 10.1038/embor.2008.233.
M. Bordons, M. Fernández, I. Gómez, “Advantages and limitations in the use of impact factor measures for the assessment of research performance”, Scientometrics, vol. 53, iss. 2,
pp. 195-206, 2002. doi: 10.1023/A:1014800407876.
T. R. Gruber, “Collective Knowledge Systems: Where the Social Web meets the Semantic Web”, Web Semantics: Science, Services and Agents on the World Wide Web, vol. 6, iss. 1, pp. 4-13, 2008. doi:10.1016/j.websem.2007.11.011.
A. Gladun, and Yu. Rogushina, “Use of Semantic Web Technologies and Multilinguistic Thesauri for Knowledge-Based Access to Biomedical Resources”, I.J. Intelligent Systems and Applications, no. 1, pp. 11-20, 2012. doi: 10.5815/ijisa.2012.01.02.
A. Gladun, and Yu. Rogushina, “Formalization of Search Context on Base of Ontologies and Multilinguistic Thesauruses”, International Journal of Computing, vol. 6, iss. 3, pp. 16-22, 2007.
A. Gladun, and Yu. Rogushina, Semantic Technologies: Principles and Practices. Кyiv, Ukraine: “ADEF Ukraine” Publishing House, 2016.
A. Gladun, Yu. Rogushina, A. Andrushevich, and A.Kurbatski, “User-oriented Recognition of Intelligent Information Objects in Distributed Dynamic Informational Web-space”, in Proc. of the 12th International Conference on Pattern Recognition and Information Processing, Minsk, 2014, pp. 1-8.
Yu. Rogushina, A. Gladun, V. Osadchiy, and S. Pryima, “Ontological analysis into the Web”. Melitopol, Ukraine: MDPU im. Bogdana Hmelnickogo, 2015.
How to Cite
Copyright (c) 2020 Collection "Information technology and security"
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors that are published in this collection, agree to the following terms:
- The authors reserve the right to authorship of their work and pass the collection right of first publication this work is licensed under the Creative Commons Attribution License, which allows others to freely distribute the published work with the obligatory reference to the authors of the original work and the first publication of the work in this collection.
- The authors have the right to conclude an agreement on exclusive distribution of the work in the form in which it was published this anthology (for example, to place the work in a digital repository institution or to publish in the structure of the monograph), provided that references to the first publication of the work in this collection.
- Policy of the journal allows and encourages the placement of authors on the Internet (for example, in storage facilities or on personal web sites) the manuscript of the work, prior to the submission of the manuscript to the editor, and during its editorial processing, as it contributes to productive scientific discussion and positive effect on the efficiency and dynamics of citations of published work (see The Effect of Open Access).