Intelligent system structure for Web resources processing and analysis
Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
Intelligent system structure for Web resources processing and analysis
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| Creator |
Lytvyn, Vasyl
Vysotska, Victoria Chyrun, Lyubomyr Smolarz, Andrzej Naum, Oleh |
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| Contributor |
Lviv Polytechnic National University
Institute of Electronics and Information Technology, Lublin University of Technology Information Systems and Technologies Department, Drohobych Ivan Franko State Pedagogical University |
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| Subject |
content analysis
information resources rating evaluation content management system ontology knowledge base machine learning intelligent agent stemming parser |
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| Description |
The paper describes the general detailed and formal description of intelligent system of information resources processing (ISIRP) based ontology. The content life cycle phase implementation of ISIRP structure is improved. The general principles of ISIRP designing structures enable automated information resource processing to increase regular user text content realization, reducing the production cycle, saving time and increasing the e-commerce capabilities.
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| Date |
2018-02-22T11:38:03Z
2018-02-22T11:38:03Z 2017 |
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| Type |
Conference Abstract
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| Identifier |
Intelligent system structure for Web resources processing and analysis / Vasyl Lytvyn, Victoria Vysotska, Lyubomyr Chyrun, Andrzej Smolarz, Oleh Naum // Computational linguistics andintelligent systems (COLINS 2017) : proceedings of the 1st International conference, Kharkiv, Ukraine, 21 April 2017 / National Technical University «KhPI», Lviv Polytechnic National University. – Kharkiv, 2017. – P. 56–74. – Bibliography: 19 titles.
http://ena.lp.edu.ua:8080/handle/ntb/39459 |
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| Language |
en
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| Relation |
1. Vysotska V., Chyrun L., Lytvyn V., Dosyn D. (2016). Methods based on ontologies for information resources processing : Monograph. LAP Lambert Academic Publishing. Saarbrucken, Germany. 2. Lytvyn V., Pukach P., Bobyk І., Vysotska V. (2016). The method of formation of the status of personality understanding based on the content analysis, Eastern-European Journal of Enterprise Technologies, no5/2(83), 4–12. 3. Bisikalo O.V., Vysotska V.A. (2016). Identifying keywords on the basis of content monitoring method in ukrainian texts, Journal «Radio Electronics, Computer Science, Control», No 1, Zaporizhzhya National Technical University, 74-83, Access mode: http://ric.zntu.edu.ua/article/view/66664/0. 4. Lytvyn V., Vysotska V., Veres O., I Rishnyak., and Rishnyak H. (2017). Classification Methods of Text Documents Using Ontology Based Approach, Advances in Intelligent Systems and Computing 512, Springer International Publishing AG: 229-240. 5. Ourania Hatzi, Dimitris Vrakas, Nick Bassiliades, (2010). Dimosthenis Anagnostopoulos, and Ioannis Vlahavas. The PORSCE II Framework: Using AI Planning for Automated Semantic Web Service Composition the Knowledge Engineering Review, Cambridge University Press, Vol. 02:3, 1–24 p. (In English) 6. Lytvyn V. (2013). Design of intelligent decision support systems using ontological approach, An international quarterly journal on economics in technology, new technologies and modelling processes, Krakiv-Lviv, Vol. II, No 1, 31 – 38 (In English). 7. Lytvyn V., Dosyn D., Smolarz A. (2013). An ontology based intelligent diagnostic systems of steel corrosion protection, Elektronika, Lodzj. – No. 8. – 2-13. – Pp. 22-24 (In English). 8. Lytvyn V. (2011), The similarity metric of scientific papers summaries on the basis of adaptive ontologies , Proceedings of VIIth International Conference on Perspective Technologies and Methods in MEMS Design, Polyana, Ukraine, pp. 162. (In English) 9. Link Grammar – Carnegie Mellon University, available at: http://bobo.link.cs.cmu.edu/link. 10. Qiu Ji, Peter Haase, and Guilin Qi (2008). Combination of Similarity Measures in Ontology Matching using the OWA Operator, In Proceedings of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge- Base Systems. 11. Gruber T. A. (1993). Translation approach to portable ontologies. Knowledge Acquisition, № 5 (2):199–220. 12. Guarino N. (1995). Formal Ontology, Conceptual Analysis and Knowledge Representation. International Journal of Human-Computer Studies, 43(5-6):625–640. 13. Sowa J. (1992). Conceptual Graphs as a universal knowledge representation. In: Semantic Networks in Artificial Intelligence, Spec. Issue of An International Journal Computers & Mathematics with Applications. (Ed. F. Lehmann), № 2–5:75–95. 14. Montes-y-Gómez M. (2000). Comparison of Conceptual Graphs. Lecture Notes in Artificial Intelligence, Vol. 1793. – Springer-Verlag, Access mode: http://ccc.inaoep.mx/~mmontesg/publicaciones/ 2000/ComparisonCG. 15. Muller H.M., Kenny E.E., Sternberg P.W. (2004). ―An Ontology-Based Information Retrieval and Extraction System for Biological Literature‖. PLoS Biol. 2(11):e309. doi:10.1371/journal.pbio.0020309. 16. Knappe R., Bulskov H., Andreasen T. (2004). Perspectives on Ontology-based Querying // International Journal of Intelligent Systems, Access mode: http://akira.ruc.dk/~knappe/publications/ijis2004.pdf. 17. Jacso, Peter. (2010). ―The impact of Eugene Garfield through the prizm of Web of Science,‖. Annals of Library and Information Studies, Vol. 57, p. 222. 18. Christoph Meinel Serge Linckels (2007). Semantic interpretation of natural language user input to improve search in multimedia knowledge base, Information Technologies, 49(1):40–48. 19. Giorgos Stoilos, Giorgos Stamou, and Stefanos Kollias (2005) A String Metric For Ontology Alignment, Proc. of the 4rd Int. Semantic Web Conf. (ISWC), vol 3729 of LNCS, p. 624–637, Berlin. Springer.
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| Format |
56-74
application/pdf |
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| Coverage |
UA
Kharkiv |
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| Publisher |
National Technical University «KhPI»
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