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Towards a Framework for Approximate Ontologies

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Currently, there is a great deal of interest in developing tools for the generation and use of ontologies on the WWW. These knowledge structures are considered essential to the success of the semantic web, the next phase in the evolution of the WWW. Much recent work with ontologies assumes that the concepts used as building blocks are crisp as opposed to approximate. It is a premise of this paper that approximate concepts and ontologies will become increasingly more important as the semantic web becomes a reality. We propose a framework for specifying, generating and using approximate ontologies. More specifically, (1) a formal framework for defining approximate concepts, ontologies and operations on approximate concepts and ontologies is presented. The framework is based on intuitions from rough set theory; (2) algorithms for automatically generating approximate ontologies from traditional crisp ontologies or from large data sets together with additional knowledge are presented. The knowledge will generally be related to similarity measurements between individual objects in the data sets, or constraints of a logical nature which rule out particular constellations of concepts and dependencies in generated ontologies. The techniques for generating approximate ontologies are parameterizable. The paper provides specific instantiations and examples.
Wydawca
Rocznik
Strony
147--165
Opis fizyczny
Bibliogr. 17 poz.
Twórcy
autor
  • Department of Computer and Information Science, Linköping University, S-581 83 Linköping, Sweden
autor
  • The College of Economics and Computer Science, Olsztyn, Poland
  • The College of Economics and Computer Science, Olsztyn, Poland
autor
  • The College of Economics and Computer Science, Olsztyn, Poland
Bibliografia
  • [1] Berners-Lee, T.: Weaving the Web, Harper Business, 2000.
  • [2] Catteano, G.: Abstract Approximation Spaces for Rough Theories, [15], 1998.
  • [3] Doherty, P., Łukaszewicz, W., Szałas, A.: Tolerance spaces and approximative representational structures, Proceedings 26th German Conference on Artificial Intelligence (A. Günter, R. Kruse, B. Neumann, Eds.), 2821, Springer-Verlag, 2003.
  • [4] Duentsch, I., Gediga, G.: Uncertainty Measures of Rough Set Prediction, Artificial Intelligence, 106, 1998, 77-107.
  • [5] Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, Springer-Verlag, 2001.
  • [6] Gruber, T. R.: A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, 5, 1993, 199-220.
  • [7] Harnad, S., Ed.: Categorical Perception: The Grundwork of Cognition, Cambridge University Press, 1987.
  • [8] Harnad, S.: Psychophysical and cognitive aspects of categorical perception: A critical overview, [7], 1987.
  • [9] Lutz, C., Wolter, F., Zakharyaschev, M.: A tableau algorithm for reasoning about concepts and similarity, Proceedings of the 12th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods TABLEAUX 2003, LNAI, Springer Verlag, 2003.
  • [10] Medin, D., Barsalou, L.: Categorization Processes in Category Structure, [7], 1987.
  • [11] Mitchell, T.: Machine Learning, Mc Graw-Hill, Portland, 1997.
  • [12] Pal, S., Peters, J., Polkowski, L., Skowron, A.: Rough-Neural Computing: An Introduction, [13], 2003.
  • [13] Pal, S., Polkowski, L., Skowron, A., Eds.: Rough-Neuro Computing: Techniques for Computing with Words, Cognitive Technologies, Springer-Verlag, 2003.
  • [14] Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
  • [15] Polkowski, L., Skowron, A., Eds.: Rough Sets in Knowledge Discovery 1: Methodology and Applications, vol. 17 of Studies in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg, 1998.
  • [16] Skowron, A., Stepaniuk, J.: Generalized approximation spaces, Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery (T. Lin, A. Wildberger, Eds.), 1995.
  • [17] Skowron, A., Stepaniuk, J.: Tolerance Approximation Spaces, Fundamenta Informaticae, 27, 1996, 245-253.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0004-0147
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