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Distributed Path-Based Inference in Semantic Networks

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Abstract

This paper presents the task model, instruction set, reasoning scheme, software infrastructure, as well as the experimental results, of a new distributed semantic network system. Unlike the synchronous and static marker passing algorithm previously used for parallel semantic network design, our system operates asynchronously, supporting knowledge sharing, dynamic load balancing and duplicate checking. To better the performance in distributed environments, the system has two collaborating components: the slave module, which performs task execution; and the host module, which interacts with the user and processes the information for the slaves. Our current implementation focuses on path-based knowledge inferences, using ANSI C and the MPICH-G2 with flex lexical analyzer and the yacc parser generator. Tests of individual components have been performed on a SUN multiprocessor server. The experiments demonstrate promising speedups.

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References

  1. R. J. Brachman and J. G. Schmolze. An overview of the KL-ONE knowledge representation system. Cognitive Sci., 9:171-216, 1985.

    Google Scholar 

  2. N. Cercone. The ECO family. Computers Math. Applic., 23(2-5):95-131, 1992.

    Google Scholar 

  3. S. Chung and D. I. Moldovan. Modeling semantic networks on the connection machine. Journal of Parallel and Distributed Computing, 17:152-163, 1993.

    Google Scholar 

  4. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. McGraw-Hill, 2000.

  5. R. F. Demara and D. I. Moldovan. The SNAP-1 parallel AI prototype. IEEE Trans. on Parallel and Distributed Systems, 4(8):841-854, 1993.

    Google Scholar 

  6. M. P. Evett, J. A. Hendler, and L. Spector. Parallel knowledge representation on the connection machine. Journal of Parallel and Distributed Computing, 22:168-184, 1991.

    Google Scholar 

  7. S. E. Fahlman. NETL: A System for Representing and Using Real-World Knowledge. The MIT Press, 1982.

  8. C. P. Ferreira, N. J. Mamede, and J. P. Martins. SNIP 2.1, The SNePS inference package. Tech. rep., Instituto Superior Técnico, Av. Rovisco Pais, 1000 Lisboa, Portugal, Jun 1989.

  9. I. Foster, The Grid: A new infrastructure for 21st century. Physics Today, 55(2):42-47, 2002.

    Google Scholar 

  10. I. Foster and C. Kesselman. Globus: A metacomputing infrastructure toolkit. Intl J. Supercomputer Applications, 11(2):115-128, 1997.

    Google Scholar 

  11. M. R. Garey, D. J. Johnson, and L. Stockmeyer. Some simplified NP-complete graph problems. Theoret. Comput. Sci., 1:237-267, 1976.

    Google Scholar 

  12. J. A. Hendler. Massively-parallel marker-passing in semantic networks. Computers Math. Applic., 23(2-5):277-291, 1992.

    Google Scholar 

  13. E. Lazowska and M. Squillante. Using processor-cache affinity in shared-memory multiprocessor scheduling. IEEE Trans. on Parallel and Distributed Systems, 4(2):131-143, 1993.

    Google Scholar 

  14. C.-W. Lee, C.-H. Huang, and S. Rajasekaran.Trojan:Ascalable parallel semantic network system. Proceedings of the 15th IEEE International Conference on Tools eith Artificial Intelligence, Sacramento, CA, 2003, pp. 219-223.

  15. D. Moldovan, W. Lee, C. Lin, and M. Chung. SNAP, Parallel processing applied to AI. IEEE Computer, 39-49, May 1992.

  16. D. I. Moldovan, M. Pasca, S. Harabagiu, and M. Surdeanu. Performance issues and error analysis in an open-domain question answering system. ACM Tran. on Information Systems, 21(2):133-154, 2003.

    Google Scholar 

  17. P. S. Pacheco. Parallel Programming with MPI. Morgan Kaufmann, 1997.

  18. S. C. Shapiro. Whose Norm? Review of Rene Elio, ed., Common Sense, Reasoning and Rationality. Trends in Cognitive Science, Oxford University Press, Oxford, UK 6(11):490, 2002.

    Google Scholar 

  19. S. C. Shapiro. Knowledge Representation. In Lynn Nadel, ed., Encyclopedia of Cognitive Science, Macmillan Publishers Ltd. 2:671-680, 2003.

  20. S. C. Shapiro and The SNEPS Implementation Group. SNePS-2.4 User's Manual. Department of Computer Science at SUNY Buffalo, 1998.

  21. K. Stoffel, J. Hendler, J. Saltz, and B. Anderson. Parka on MIMD-Supercomputers. Tech. Rep. CS-TR-3672, Computer Science Dept., UM Institute for Advanced Computer Studies, University of Maryland, College Park, 1996.

    Google Scholar 

  22. M. Surdeanu, D. I. Moldovan, and S. M. Harabagiu. Performance analysis of a distributed question/answering system. IEEE Trans. on Parallel and Distributed Systems 13(6):579-596, 2002.

    Google Scholar 

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Lee, CW., Huang, CH., Yang, L.T. et al. Distributed Path-Based Inference in Semantic Networks. The Journal of Supercomputing 29, 211–227 (2004). https://doi.org/10.1023/B:SUPE.0000026852.08638.96

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  • DOI: https://doi.org/10.1023/B:SUPE.0000026852.08638.96

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