Use of the Ontological Model for Personification of the Semantic Search

Автор: J. Rogushina

Журнал: International Journal of Mathematical Sciences and Computing(IJMSC) @ijmsc

Статья в выпуске: 1 vol.2, 2016 года.

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Semantic search is an important component of modern intelligent applications oriented on work in open information environment. The intelligence level of application depends of it's capabilities in knowledge processing and defines it's facilities. Now applications widely use ontologies for knowledge representation. Therefore criteria of intelligence level estimation (that can analyze ontologies) of applications and retrieval systems as their particular case are proposed. In this paper, an ontological model of the intelligent interaction of the main objects and subjects of the semantic search (the Web information resources, information objects and information consumers etc.) is developed. Software realization of semantic search on base of this ontological model and integration of this search instrument with applied systems are describes.

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Semantic search, information object, ontological model, thesaurus, collaborative search

Короткий адрес: https://sciup.org/15010194

IDR: 15010194

Список литературы Use of the Ontological Model for Personification of the Semantic Search

  • Gladun A, Rogushina J, Valencia-García R, Martínez-Béjar R Semantics-driven modelling of user preferences for information retrieval in the biomedical domain. Informatics for health and social care. V.38, N.2 2013: P.150-170.
  • Minsky M Progress Report on Artificial Intelligence. Seymour Papert ec 11, 1971. – http:// web.media.mit.edu/~minsky/papers/PR1971.html.
  • Russell S, Norvig P Artificial intelligence: a modern approach. Upper Saddle River, New Jersey: Prentice Hall, 2003.
  • Nilsson N Artificial Intelligence: A New Synthesis. Morgan Kaufmann. 1998.
  • Luger GF Stubblefield W Artificial intelligence: structures and strategies for complex problem solving. Pearson education. 2005.
  • Negnevitsky, Michael. Artificial intelligence: a guide to intelligent systems. Pearson Education, 2005.
  • Nayak R, Ichalkaranje N, Jain LC. eds. Evolution of the Web in Artificial Intelligence Environments, Vol. 130. Springer, 2008.– 277 p.
  • Lyubich AA, Pleskach VL, Rogushina JVAbout selection of criteria for evaluation of information system intelligence. UsiM, № 1, 2005:3-7. (In Russian).
  • Gavrilova TA, Horoshevsky VF Knowledge bases of intelligent systems. St. Petersburg: Piter. – 2001. – 382 p. (In Russian).
  • Kuzmenko GE, Litvinov VA Pragmatic approach to estimation of intelligence level on intelligent systems. Mathematical machines and systems. № 1, 2003:3-9. (In Ukrainian).
  • Amerland D. Google Semantic Search: Search Engine Optimization (SEO) Techniques That Gets Your Company More Traffic, Increases Brand Impact and Amplifies Your Online Presence. Que Publishing, 2013. – 230 p.
  • Davies J, Fensel D, van Harmelen F Towards the Semantic Web: Ontology-driven knowledge management. John Wiley & Sons Ltd,, England, 2002. – 288 p.
  • Staab S, Studer, R. (Eds.) Handbook on ontologies. Springer Science & Business Media. 2013.
  • Rao AS, Georgeff MP Modeling rational agents within a BDI-architecture. In R. Pikes and E. Sandewall, eds.. Proc. of Knowledge Representation and Reasoning (KR&R-91), Morgan Kaufmann Publishers: San Mateo, CA, 1991:473-484.
  • Rogushina J, Gladun A Ontology-based competency analyses in new research domains. Journal of Computing and Information Technology. V.20, N. 4, 2012:277-293.
  • Ricci F, Rokach L, Shapira B, Kantor P Recommender Systems Handbook. Springer, 2011. – 842 p.
  • Gladun A, Rogushina J, Schreurs J, Salem Abdel-Badeeh Ontology-based knowledge recognition in service-oriented virtual research environments. Proc. of The 7th International Conference on Information Technology ICIT-2015, Al Zaytoonah University of Jordan, Amman, Jordan, 2015:148-155.
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