Abstract
Diagnostic knowledge about chosen technical classes of objects can be effective gained by analyzing Internet webpages. In this paper for analyzing these data is proposed the data granulation method. Information granules are mathematical models describing data aggregates. Data aggregates are connected with each other and described by the Fuzzy Description Logic. It is presented that this data granulation model can be used to sharpen the diagnostic knowledge.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
1. Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic. Handbook Theory, Implementation and Application. Cambridge University Press, Cambridge (2003)
2. Baumeister J.: Agile Development of Diagnostic Knowledge Systems. infix, Akademische Verlagsgesellschaft Aka GmbH, Berlin (2004)
3. Belard N., Pencol’e Y., Combacau M.: A theory of meta-diagnosis: Reasoning about diagnostic systems. In Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI11), pp. 731–737, (2011)
4. Bloch I.: Mathematical Morphology. In: Handbook of Spatial Logics, M. Aiello, I.Pratt-Hartmann and J. van Benthem (eds.), pp. 857–944, Springer (2007)
5. Bobillo, F., Straccia, U.: fuzzyDL: An expressive fuzzy description logic reasoner.In: Proc. IEEE Int. Conference on Fuzzy Systems FUZZ-IEEE 2008 (IEEEWorld Congress on Computational Intelligence), pp. 923–930, (2008)
6. Bryniarska, A.: The Paradox of the Fuzzy Disambiguation in the Information Retrieval. (IJARAI) International Journal of Advanced Research in Artificial Intelligence, pp. 55–58, Volume 2 No 9, (2013)
7. Bryniarska, A.: The Model of PossibleWeb Data Retrieval. Proceedings of 2nd IEEE International Conference on Cybernetics CYBCONF 2015, pp. 348–353, (2015)
8. Bryniarska, A.: An Uncertain Diagnostic System of the Constructional and Technological Preferences. Proc. Of The 21st International Conference on Methods and Models in Automation and Robotics MMAR 2016, pp. 256–260, (2016)
9. Fanizzi, N., d’Amato, C., Esposito, F., Lukasiewicz, T.: Representing uncertain concepts in rough description logics via contextual indiscernibility relations. In: Bobillo, F., da Costa, P.C.G., d’Amato, C., et al. (eds.) Proc. 4th Int. Workshop on Uncertainty Reasoning for the Semantic Web, (2008)
10. Kosko B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, N.J. (1992)
11. Pedrycz, W.: Allocation of information granularity in optimization and decision-making models: towards building the foundations of Granular Computing, pp. 137–145, (2014)
12. Pedrycz, W.: Granular computing: analysis and design of intelligent systems. Taylor & Francis Group, Abingdon (2013)
13. Simou, N., Mailis, T., Stoilos, G., Stamou, S.: Optimization techniques for fuzzy description logics. In: Description Logics. Proc. 23rd Int.Workshop on Description Logics (DL 2010). CEUR-WS, vol. 573, (2010)
14. Serra J.: Image Analysis and Mathematical Morphology. Academic Press (1982)
15. Skowron A., Swiniarski R., Synak P.: Approximation Spaces and Information Granulation. In: J.F. Peters and A. Skowron (Eds.): Transactions on Rough Sets III, LNCS 3400, pp. 175–189, Springer-Verlag Berlin Heidelberg (2005)
16. Yao, Y.Y. : The art of granular computing. In: Proceeding of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms LNAI 4585, pp. 101–112, (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bryniarska, A. (2017). A data granulation model for searching knowledge about diagnosed objects. In: Mitkowski, W., Kacprzyk, J., Oprzędkiewicz, K., Skruch, P. (eds) Trends in Advanced Intelligent Control, Optimization and Automation. KKA 2017. Advances in Intelligent Systems and Computing, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-60699-6_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-60699-6_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60698-9
Online ISBN: 978-3-319-60699-6
eBook Packages: EngineeringEngineering (R0)