Designing medical production rules from semantically integrated data

• Autorzy: Jankowska B. , Szymkowiak M.
• Języki publikacji: EN

Abstrakty

EN

In the paper an algorithm for automatic knowledge acquisition is proposed. The knowledge is acquired from aggregate data stored in different repositories. The algorithm operates by means of semantic data integration, allowing both syntax and semantic differences between data coming from different sources. If only we know data taxonomies, can interpret data schemas and design schema mappings, then the differences are not an obstacle to integration. The acquired knowledge is being defined in a form of production rules with uncertainty. The considerations are illustrated with medical examples.

Słowa kluczowe

EN

semantic data integration; medical rule-based systems; uncertainty

PL

integracja danych semantycznych; medyczne systemy oparte na regułach; niepewność

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 16
• Strony: 95--102
• Opis fizyczny: Bibliogr. 18 poz., tab.

Twórcy

autor

Jankowska B.

• Institute of Control and Information Engineering, Pl. Skłodowskiej–Curie 5, 60-965 Poznań, Poland

autor

Szymkowiak M.

Bibliografia

• [1] AGRAWAL R., IMIELINSKI T., SWAMI A., Mining Association Rules Between Sets of Items in Large Databases, SIGMOD Record 22(2), 1993, pp. 805-810.
• [2] AGRAWAL R., SRIKANT R., Fast algorithms for mining association rules in large databases, in: Bocca J.B., Jarke M., Zaniolo C. (Eds.), Proc. of the 20th Int.Conference on Very Large Data Bases, Morgan Kaufmann, Santiago de Chile, 1994, pp. 487-499.
• [3] ARENAS M., LIBKIN L., XML Data Exchange: Consistency and Query Answering, in: LI Ch. (Ed.), Proc. of the 24th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, ACM, Baltimore, 2005, pp.13-24.
• [4] CHEN H., Rewriting Queries Using View for RDF/RDFS-Based Relational Data Integration, LNCS 3816, 2005, pp. 243-254.
• [5] GRZYMALA-BUSSE J., GRZYMALA-BUSSE W., An experimental comparison of three rough set approaches to missing attribute values, Trans. on Rough Sets 6, 2007, pp. 1-47.
• [6] HAN J., PEI J., YIN Y., MAO R., Mining frequent patterns without candidate generation, Data Mining and Knowledge Discovery 8, 2004, pp. 53-87.
• [7] HAAS L.M., HERNANDEZ M.A., HO H., POPA L., ROTH M., Clio grows up: from research prototype to industrial tool, in: Özcan F. (Ed.), Proc. of ACM SIGMOD Conference on Management of Data, ACM, Baltimore, 2005, pp. 805-810.
• [8] JANKOWSKA B., On Integrating Medical Data by means of an Algebraic Lattice, in: Informatyczne systemy zarządzania wiedzą, Akademicka Oficyna Wydawnicza EXIT, (in Polish).
• [9] JANKOWSKA, B., SZYMKOWIAK, M.: How to Acquire and Structuralize Knowledge for Medical Rule-Based Systems?, in: Studies in Computational Intelligence, Kacprzyk J. (ed.), Springer Series, Berlin /Heidelberg, 2008, pp. 115-130
• [10] MILLER R.J. et al., The Clio Project: Managing Heterogeneity, SIGMOD Record 30(1), 2001, pp. 77-83.
• [11] OWL Web Ontology Language, http://www.w3.org/TR/2004/REC-owl-features-20040210/, 2010.
• [12] PANKOWSKI T., XML data integration in SixP2P - a theoretical framework, in: Doucet A., Gançarski S., Pacitti E., (Eds.), Proc. of the 2008 Int. Workshop on Data Management in Peer-to-Peer Systems, ACM Series, Nantes, 2008, pp. 11-18.
• [13] PAWLAK Z., Rough sets, Int. Journal of Information & Computer Science 11, 1982, pp. 341-356.
• [14] PLOTNICK L.H., DUCHARME F.M., Combined inhaled anticholinergics and beta2-agonists for initial treatment of acute asthma in children, in: The Cochrane Library, 2005.
• [15] PREDKI B., SLOWINSKI R., STEFANOWSKI J., SUSMAGA R., WILK Sz., ROSE. Software Implementation of the Rough Set Theory, LNCS 1424, 1998, pp. 605-608.
• [16] SZYMKOWIAK, M., JANKOWSKA, B.: Reliability of Medical Production Rules Obtained by means of Aggregate Data Mining, submitted to the conf. MIT 2010.
• [17] The Protégé Ontology Editor, http://protege.stanford.edu/, 2010.
• [18] ZAKI M.J., Scalable algorithms for association mining, IEEE Trans. on Knowledge and Data Engineering 12(3), 2000, pp.372-390.