Abstract
Knowledge Cartography (KC) allows for fast answering of Description Logic (DL) knowledge base queries, but requires expensive preprocessing to represent knowledge in internal representation, i.e., the algorithm for computation of map of concepts as binary signatures is exponential time (however, for taxonomies—as many practical cases have shown—it is at most quadratic time). Preprocessing is already part of DL reasoning process and some computations are pre-calculated before user issues query to knowledge base. Using another method—Tableaux, no knowledge preprocessing is performed, however, all reasoning is done after user issues query. That’s why KC is faster than Tableaux during query answering. The chapter focuses on preprocessing issue for KC. It mainly considers the research on efficient generation of binary signatures and signatures rebuilding by employing the methods for logic synthesis. It has been confirmed that logic synthesis Complement algorithm is efficient when applied to the construction of the map of concepts. The research has shown that strategy of construction should be adjusted depending on ontology size. For smaller ontologies—the non-recursive approach should be used, on the contrary—for larger ontologies—recursive approach with bi-partitioning of the ontology graph. The recursive procedure indicated good scaling for large taxonomies. Another observation was that Complement algorithm works faster for non-sorted CNFs.
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References
Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation and Applications, 2nd edn. Cambridge University Press, Cambridge (2007)
Borowik, G.: Boolean function complementation based algorithm for data discretization. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) Computer Aided Systems Theory—EUROCAST 2013, Part II. LNCS, vol. 8112, pp. 218–225. Springer, Heidelberg (2013). doi:10.1007/978-3-642-53862-9_28
Borowik, G.: Data mining approach for decision and classification systems using logic synthesis algorithms. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds.) Advanced Methods and Applications in Computational Intelligence, Chap. 1. Topics in Intelligent Engineering and Informatics, pp. 3–23. Springer International Publishing (2014). doi:10.1007/978-3-319-01436-4_1
Borowik, G., Łuba, T.: Fast algorithm of attribute reduction based on the complementation of Boolean function. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds.) Advanced Methods and Applications in Computational Intelligence, Chap. 2. Topics in Intelligent Engineering and Informatics, pp. 25–41. Springer International Publishing (2014). doi:10.1007/978-3-319-01436-4_2
Borowik, G., Nogalski, D.: Efficient technique for transformation from concepts to Boolean algebra. In: Chaczko, Z., Gaol, F.L., Pichler, F., Chiu, C. (eds.) 2nd Asia-Pacific Conference on Computer Aided System Engineering—APCASE 2014, pp. 24–27. APCASE Foundation, Bali (2014)
Brayton, R.K., Hachtel, G.D., McMullen, C.T., Sangiovanni-Vincentelli, A.: Logic Minimization Algorithms for VLSI Synthesis. Kluwer Academic Publishers (1984)
Goczyła, K., Grabowska, T., Waloszek, W., Zawadzki, M.: The knowledge cartography—a new approach to reasoning over description logics ontologies. In: Wiedermann, J., Tel, G., Pokorný, J., Bieliková, M., Štuller, J. (eds.) SOFSEM 2006: Theory and Practice of Computer Science. Lecture Notes in Computer Science, vol. 3831, pp. 293–302. Springer, Berlin (2006). doi:10.1007/11611257_27
Grzymała-Busse, J.W.: LERS—a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support— Handbook of Application and Advanced of the Rough Sets Theory, Theory and Decision Library, vol. 11, pp. 3–18. Springer, Netherlands (1992). doi:10.1007/978-94-015-7975-9_1
Grzymała-Busse, J.W.: LERS—a data mining system. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 1347–1351. Springer (2005). doi:10.1007/0-387-25465-X_65
Karypis, G., Kumar, V.: Parallel multilevel k-way partitioning scheme for irregular graphs. (1999)
Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: A Tutorial. (1999)
Lang, C., Steinbach, B.: Bi-decomposition of function sets in multiple-valued logic for circuit design and data mining. Artif. Intell. Rev. 20(3–4), 233–267 (2003). doi:10.1023/B:AIRE.0000006608.31990.cd
Michie, D.: Problem decomposition and the learning of skills. In: Lavrac, N., Wrobel, S. (eds.) Machine Learning: ECML-95. Lecture Notes in Computer Science, vol. 912, pp. 17–31. Springer, Berlin Heidelberg (1995). doi:10.1007/3-540-59286-5_46
Nilsson, U., Małuszyński., J.: Logic, Programming and Prolog. Previously published by John Wiley & Sons Ltd., 2 edn. (2000)
Nogalski, D., Chmielewski, M.: Semantic web service discovery and information fusion using OWL-S and SPARQL formal specifications over NATO JC3IEDM and TIDE services. In: Amanowicz, M. (ed.) Concepts and Implementations for Innovative Military Communications and Information Technologies, pp. 165–174. Military University of Technology, Warsaw (2010)
Nogalski, D., Najgebauer, A.: Semantic mediation of NATO C2 systems based on JC3IEDM and NFFI ontologies. In: RTO Symposium “Semantic and Domain based Interoperability” RTO-MP-IST-101. Oslo (2011)
Nogalski, D., Wróblewska, A., Szklarz, P., Chmielewski, M.: Battlefield situational awareness—generating usefull ontology based on relational data model MIP JC3IEDM (in Polish). In: Ficoń, K. (ed.) Transformacja Systemów Dowodzenia, pp. 177–194. Ośrodek Badawczo-Rozwojowy Centrum Techniki Morskiej (2010)
Papadimitriou, C.H.: Computational Complexity. Academic Internet Publishers (2007)
Pawlak, Z.: Rough Sets Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)
Rokach, L., Maimon, O.: Data mining using decomposition methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 981–998. Springer (2010). doi:10.1007/978-0-387-09823-4_51
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, New jersey (2009)
Samuel, A.L.: Some studies in machine learning using the game of checkers. II—recent progress. IBM J. Res. Dev. 11(6), 601–617 (1967)
Waloszek, W.: Structural representation of description logics ontologies (in Polish). Ph.D. Thesis, Gdańsk University of Technology, Gdańsk (2007)
Zupan, B., Bohanec, M., Demsar, J., Bratko, I.: Feature transformation by function decomposition. IEEE Intell. Syst. Appl. 13(2), 38–43 (1998). doi:10.1109/5254.671090
NCBO: SNOMED Clinical Terms. http://bioportal.bioontology.org/ontologies/SNOMEDCT (2014). Accessed May 2014
RSES—Rough Set Exploration System. http://logic.mimuw.edu.pl/~rses/. Accessed May 2014
SW: W3C Semantic Web Activity. http://www.w3.org/2001/sw/. Accessed May 2014
UC Irvine machine learning repository. http://archive.ics.uci.edu/ml/. Accessed May 2014
W3C: OWL Web Ontology Language. http://www.w3.org/TR/owl-features/. Release date Feb 2004
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Borowik, G., Nogalski, D. (2015). Technique for Transformation of DL Knowledge Base to Boolean Representation. In: Borowik, G., Chaczko, Z., Jacak, W., Łuba, T. (eds) Computational Intelligence and Efficiency in Engineering Systems. Studies in Computational Intelligence, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-15720-7_3
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