Abstract
In this paper we assume there is a group of connected distributed information systems (DIS). They work under the same ontology. Each information system has its own knowledgebase. Values of attributes in incomplete information system \( IS \) form atomic expressions of a language used for communication with others. Collaboration among systems is initiated when one of them is asked to resolve a query containing nonlocal attributes for \( IS \). When query fails, then the query answering system (QAS) is trying to replace values in a query by new values from their corresponding neighborhoods. QAS for IS can also collaborate and exchange knowledge with other information systems. In all such cases, it is called intelligent. As the result of its request, knowledge is extracted locally in each information system and sent back to the client. The outcome of this step is collective knowledgebase. In this paper we present a method of identifying which information system is semantically the closest to IS. We propose a new measure supporting choice of closest pair of systems, which determines the distance between the two systems. The proposed method was tested and verified in medical systems with randomly selected data. The satisfying initial results were obtained and based on them, the proposed measure can be successfully used in medical systems to support the work of doctors and the treatment of patients.
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References
Glebocka, A.D.: Null values and chase in distributed information systems. In: Negoita, Mircea Gh., Howlett, Robert J., Jain, Lakhmi C. (eds.) KES 2004. LNCS (LNAI), vol. 3214, pp. 1143â1149. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30133-2_152
Dardzinska, A.: Action Rules Mining, pp. 5â19. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-35650-6
Dardzinska, A., Ignatiuk, K., Zdrodowska, M.: Query answering system as a tool in incomplete distributed information system optimization process. In: Dang, T.K., Wagner, R., KĂŒng, J., Thoai, N., Takizawa, M., Neuhold, Erich J. (eds.) FDSE 2017. LNCS, vol. 10646, pp. 101â109. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70004-5_7
DardziĆska, A., RaĆ, Zbigniew W.: CHASE2 â rule based chase algorithm for information systems of type λ. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 255â267. Springer, Heidelberg (2005). https://doi.org/10.1007/11423270_14
Dardzinska, A., Ras, Z.: Extracting rules from incomplete decision systems: system ERID. In: Young Lin, T., Ohsuga, S., Liau, C.J., Hu, X. (eds.) Foundations and Novel Approaches in Data Mining, pp. 143â153. Springer, Heidelberg (2006). https://doi.org/10.1007/11539827_8
Guarino, N.: Formal ontology in information systems. In: Proceedings of FOIS 1998, pp. 3â15, Trento, Italy (1998)
Guarino, N., Giaretta, P.: Ontologies and knowledge bases, towards a terminological clarification. Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, pp. 25â32 (1995)
Laudon K., Laudon J.: Management Information System: Managing the Digital Firm, pp. 14â16. Prentice Hall, New Jersey (2012)
Mizoguchi, R.: Tutorial on ontological engineeringâPart 1: introduction to ontological engineering. New Gener. Comput. 21(4), 365â384 (2003)
Pawlak, Z.: Information systemsâtheoretical foundations. Inf. Syst. J. 6(1981), 205â218 (1991)
Ras, Z.: Collaboration control in distributed knowledge-based system. Inf. Sci. 96(3), 193â205 (1997)
Ras, Z.: Query answering based on distributed knowledge mining. In: Proceedings of the 2nd Asia-Pacific Conference on Intelligent Agent Technology: Research and Development, pp. 17â27, Maebashi City, Japan (2001)
Ras, Z.: Reducts-driven query answering for distributed knowledge systems. Int. J. Intell. Syst. 17(2), 113â124 (2002)
Ras, Z., Dardzinska, A.: Solving failing queries through cooperation and collaboration. World Wide Web J. 9(2), 173â186 (2006)
Ras, Z., Dardzinska, A.: Cooperative multi-hierarchical query answering systems. In: Meyers, R. (ed.) Encyclopedia of Complexity and Systems Science, pp. 1532â1537. Springer, New York (2009). https://doi.org/10.1007/978-0-387-30440-3_100
Ras, Z., Joshi, S.: Query approximate answering system for an incomplete DKBS. Foundamenta Informaticae J. 30(3), 313â324 (1997)
Van Heijst, G., Schreiber, A., Wielinga, B.: Using explicit ontologies in KBS development. Int. J. Hum. Comput. Stud. 46(2), 183â292 (1997)
Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J., Hua, L.: Data mining in healthcare and biomedicine: a survey of the literature. J. Med. Syst. 36(4), 2431â2448 (2012)
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Research was performed as a part of project no. MB/WM/6/2017 and financed with use of funds for science of MNiSW.
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Ignatiuk, K., Dardzinska, A., Zdrodowska, M., Chorazy, M. (2018). New Method of Medical Incomplete Information System Optimization Based on Action Queries. In: Dang, T., KĂŒng, J., Wagner, R., Thoai, N., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2018. Lecture Notes in Computer Science(), vol 11251. Springer, Cham. https://doi.org/10.1007/978-3-030-03192-3_24
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