Advertisement

Applying Caching Capabilities to Inference Applications Based on Semantic Web

  • Alejandro Rodríguez
  • Enrique Jimenez
  • Mateusz Radzimski
  • Juan Miguel Gómez
  • Giner Alor
  • Rubén Posada-Gomez
  • Jose E. Labra Gayo
Part of the Studies in Computational Intelligence book series (SCI, volume 244)

Abstract

Nowadays there is a large number of Expert Systems available to users requiring the extraction of data relevant to specific domains, many of which are based on reasoning and inference. However, many of these tools offer slow execution time, resulting in delayed response times to the queries made by users. The strategy of caching to define specific patterns of results enables such systems to eliminate the requirement to repeat the same queries, speeding up the response time and eliminating redundancy. This paper proposes a caching strategy for an Expert System based on Semantic Web and reasoning and inference techniques. Caching strategies have previously been applied to simple XML queries. Performance has been evaluated using an existing system for medical diagnosis, which demonstrates the increased efficiency of the system.

Keywords

caching semantic web ontologies inference rule pattern recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Franklin, M.J.: Client Data Caching: A Foundation for High Performance Object Database Systems. Kluwer Academic Publishers, Dordrecht (1996)Google Scholar
  2. 2.
    Alonso, R., Barbara, D., Garcia-Molina, H.: Data caching issues in an information retrieval system. ACM Transactions on Database Systems 15(3), 359–384 (1990)CrossRefGoogle Scholar
  3. 3.
    Johnson, T., Shasha, D.: 2Q: A Low Overhead High Performance Buffer Management Replacement Algorithm. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 439–450 (1994)Google Scholar
  4. 4.
    Bei, Y., Chen, G., Hu, T., Dong, J.: Caching System for XML Queries Using Frequent Query Patterns. In: Shen, W., Yong, J., Yang, Y., Barthès, J.-P.A., Luo, J. (eds.) CSCWD 2007. LNCS, vol. 5236. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Yang, L.H., Lee, M.L., Hsu, W., Huang, D., Wong, L.: Efficient mining of frequent XML query patterns with repeating-siblings. Inf. Softw. Technol. 50(5), 375–389 (2008)CrossRefGoogle Scholar
  6. 6.
    Adali, S., Candan, S., Papakonstantinou, Y., Subrahmanyan, V.: Query Caching and Optimization in Mediator Systems. Technical Report. Stanford University (1995)Google Scholar
  7. 7.
    Ren, Q., Dunham, M.: Semantic caching and query processing. Technical Report 98-CSE-4. Southern Methodist University (May 1998)Google Scholar
  8. 8.
    Godfrey, P., Gryz, J.: Semantic query caching for heterogeneous databases. In: Proceedings of 4th KRDB Workshop at VLDB (1997)Google Scholar
  9. 9.
    Gruber, T.R.: Toward Principles for the Design of Ontologies used for Knowledge Sharing. International Journal of Human-Computer Studies 43, 907–928 (1995)CrossRefGoogle Scholar
  10. 10.
    Guarino, N.: Formal Ontology in Information Systems. In: Proceedings of the 1st International Conference on Formal Ontologies in Information Systems FOIS, pp. 3–15. IOS Press, Amsterdam (1998)Google Scholar
  11. 11.
    Hayes-Roth, F., Waterman, D.A., Lenat, D.B.: Building expert systems (1983)Google Scholar
  12. 12.
    Girardi, R., Faria, C., Marinho, L.: Ontology-based Domain Modeling of Multi-Agent Systems. In: Gonzalez-Perez, C. (ed.) Proceedings of the Third International Workshop on Agent-Oriented Methodologies at International Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA 2004), Vancouver, Canada, pp. 51–62 (2004)Google Scholar
  13. 13.
    Luke, S., Spector, L., Rager, D., Hendler, J.: Ontology-based Web Agents. International Conference on Autonomous Agents. Marina del Rey, California, United States (1997)Google Scholar
  14. 14.
    Ruay-Shiung, C., Hui-Ping, C., Yun-Ting, W.: A Dynamic Weighted Data Replication Strategy in Data Grids. In: IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2008 (2008)Google Scholar
  15. 15.
    Hanli, W., Kwong, S., Yaochu, J., Wei, W., Kim-Fung, M.: Agent-based evolutionary approach for interpretable rule-based knowledge extraction. Systems, Man, and Cybernetics, Part C 35(2), 143–155 (2005)CrossRefGoogle Scholar
  16. 16.
    Rodriguez, A., Mencke, M., Alor Hernandez, G., Posada Gomez, R., Gomez, J.M.: Medboli: Medical Diagnosis Based on Ontologies and Logical Inference. In: The Third International Conference on Digital Society, ICDS 2009, Cancun, Mexico, February 1 - 7 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alejandro Rodríguez
    • 1
  • Enrique Jimenez
    • 1
  • Mateusz Radzimski
    • 1
  • Juan Miguel Gómez
    • 1
  • Giner Alor
    • 2
  • Rubén Posada-Gomez
    • 2
  • Jose E. Labra Gayo
    • 3
  1. 1.Department of Computer ScienceUniversidad Carlos III de MadridMadrid
  2. 2.Division of Research and Postgraduate StudiesInstituto Tecnológico de OrizabaMexico
  3. 3.Department of Computer ScienceUniversidad de Oviedo 

Personalised recommendations