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Tytuł artykułu

Paradigmatic and Syntagmatic Relations in Information Systems over Ontological Graphs

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Rough Set Theory Workshop (RST’2015); (6; 29-06-2015; University of Warsaw )
Języki publikacji
EN
Abstrakty
EN
The main goal of the paper is to show the idea of incorporating paradigmatic and syntagmatic relations into processing data stored in information tables using rough set methods. Input data, in a tabular form, are used in many machine learning and computational intelligence methods and algorithms, among others, those based on rough set theory. Additional knowledge about semantic relations (both paradigmatic and syntagmatic) can be considered as a useful context affecting data classification or clustering.
Wydawca
Rocznik
Strony
229--242
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
  • Chair of Computer Science, Faculty of Mathematics and Natural Sciences, University of Rzeszów, Prof. S. Pigonia Str. 1, 35-310 Rzeszów, Poland
Bibliografia
  • [1] Joint Academic Coding System (JACS) Available from: https://www.hesa.ac.uk/jacs3.
  • [2] RAMON, Eurostat’s metadata server. Available from: http://ec.europa.eu/eurostat/ramon/nomenclatures.
  • [3] Dubois D, Prade H. Rough Fuzzy Sets and Fuzzy Rough Sets. International Journal of General Systems, 1990;17(2-3):191–209. doi: 10.1080/03081079008935107.
  • [4] Fellbaum C (ed). WordNet - An Electronic Lexical Database. MIT Press, 1998. ISBN- 13:978-0262061971, 10:026206197X.
  • [5] Matusiewicz Z, Pancerz K. Rough Set Flow Graphs and Max - * Fuzzy Relation Equations in State Prediction Problems. Rough Sets and Current Trends in Computing (C.-C. Chan, J. W. Grzymala-Busse, W. Ziarko, Eds.), vol. 5306 of Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, 2008, pp.359–368. doi: 10.1007/978-3-540-88425-5_37.
  • [6] Murphy ML (ed). Semantic relations and the lexicon: antonymy, synonymy, and other paradigms. Cambridge University Press, Cambridge, UK, 2003. ISBN- 1139437453, 9781139437455.
  • [7] Musen M et al. The Protege project: A look back and a look forward. AI Matters, 2015;1(4):4–12. doi:10.1145/2757001.2757003.
  • [8] Nastase V, Nakov P, Séaghdha DO, Szpakowicz S. Semantic Relations Between Nominals. Morgan & Claypool Publishers, 2013. doi: 10.2200/S00489ED1V01Y201303HLT019.
  • [9] Neches R, Fikes R, Finin T, Gruber T, Patil R, Senator T, Swartout W. Enabling Technology for Knowledge Sharing. AI Magazine, 1991;12(3):36–56. Available from: http://dl.acm.org/citation.cfm?id=123768.123775.
  • [10] Pancerz K. Toward Information Systems over Ontological Graphs, in: Rough Sets and Current Trends in Computing (J. Yao, Y. Yang, R. Słowiński, S. Greco, H. Li, S. Mitra, L. Polkowski, Eds.), vol. 7413 of Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin Heidelberg, 2012, pp.243–248. doi: 10.1007/978-3-642-32115-3_29.
  • [11] Pancerz K, Lewicki A, Tadeusiewicz R, Warchoł J. Ant-Based Clustering in Delta Episode Information Systems Based on Temporal Rough Set Flow Graphs. Fundamenta Informaticae, 2013;128(1-2):143–158. doi: 10.3233/FI-2013-938.
  • [12] Pancerz K, Schumann A. Rough Set Models of Physarum Machines. International Journal of General Systems, 2015;44(3):314–325. doi: 10.1080/03081079.2014.997529.
  • [13] Pawlak Z. Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, 1991. ISBN- 978-0-7923-1472-1, 978-94-010-5564-2.
  • [14] Pawlak Z. Flow Graphs and Data Mining, in: Transactions on Rough Sets III (J. Peters, A. Skowron, Eds.), vol. 3400 of Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, 2005, pp.1–36. doi: 10.1007/11427834_1.
  • [15] Pawlak Z, Skowron A. Rudiments of rough sets. Information Sciences, 2007;177(1):3–27. doi: 10.1016/j.ins.2006.06.003.
  • [16] Peters I, Weller K. Paradigmatic and Syntagmatic Relations in Knowledge Organization Systems, Information. Wissenschaft & Praxis, 2008;59(2):100–107. Available from: http://www.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/56/1204547334paradigmat.pdf.
  • [17] de Saussure F. Course in General Linguistics, Open Court, Chicago and La Salle, 1986.
  • [18] Schumann A, Pancerz K. Roughness in Timed Transition Systems Modeling Propagation of Plasmodium, in: Rough Sets and Knowledge Technology (D. Ciucci, G. Wang, S. Mitra, W.-Z. Wu, Eds.), vol. 9436 of Lecture Notes in Artificial Intelligence, Springer International Publishing, 2015, pp.482–491. doi: 10.1007/978-3-319-25754-9_42.
  • [19] Stock WG. Concepts and Semantic Relations in Information Science, Journal of the American Society for Information Science and Technology, 2010;61(10):1951–1969. doi: 10.1002/asi.v61:10.
  • [20] Yao Y. Probabilistic rough set approximations, International Journal of Approximate Reasoning, 2008; 49(2):255–271. doi: 10.1016/j.ijar.2007.05.019.
  • [21] Ziarko W. Variable Precision Rough Set Model, Journal of Computer and System Sciences, 1993; 46(1):39–59. doi: 10.1016/0022-0000(93)90048-2.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3ccad5b2-508c-420e-b695-e97824e685e5
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