This Article 
 Bibliographic References 
 Add to: 
Classification and Retrieval of Knowledge on a Parallel Marker-Passing Architecture
October 1993 (vol. 5 no. 5)
pp. 753-761

Frame-based systems or semantic networks have been generally used for knowledge representation. In such a knowledge representation system, concepts in the knowledge base are organized based on the subsumption relation between concepts, and classification is a process of constructing a concept hierarchy according to the subsumption relationships. Since the classification process involves search and subsumption test between concepts, classification on a large knowledge base may become unacceptably slow, especially for real-time applications. In this paper, a massively parallel classification and property retrieval algorithm on a marker passing architecture is presented. The subsumption relation is first defined by using the set relationship, and the parallel classification algorithm is described based on that relationship. In this algorithm, subsumption test between two concepts is done by parallel marker passing and multiple subsumption tests are performed simultaneously. To investigate the performance of the algorithm, time complexities of sequential and parallel classification are compared. Simulation of the parallel classification algorithm was performed using the SNAP (Semantic Network Array Processor) simulator, and the influence of several factors on the execution time is discussed.

[1] D. G. Bobrow and T. Winograd, "An overview of KRL: A knowledge representation language,"Cognitive Science, vol. 1, 1977.
[2] R.J. Brachman and H. J. Levesque, "The tractability of subsumption in frame-based description languages," inProc. AAAI 84, Nat. Conf. Artificial Intelligence, 1984.
[3] R. J. Brachman and J. G. Schmolze, "An overview of the KL-ONE knowledge representation system,"Cognitive Science, vol. 9, 1985.
[4] E. Charniak, "Passing markers: A theory of contextual influence in language comprehension,"Cognitive Science, vol. 7, 1983.
[5] S. E. Fahlman,NETL: A System for Representing and Using Real-World Knowledge. Cambridge, MA: The MIT Press, 1979.
[6] J.A. Hendler,Integrating Marker-Passing and Problem-Solving, Lawrence Erlbaum Associates, Hillsdale, N.J., 1988.
[7] W. D. Hillis,The Connection Machine. Cambridge, MA: MIT Press, 1985.
[8] J.-T. Kim and D. I. Moldovan, "Parallel knowledge classification on SNAP",Proc. ICPP 90, Int. Conf. Parallel Processing, 1990.
[9] C. Lin and D. I. Moldovan, "SNAP simulator results,"Tech. Rep. CENG 89-11, University of Southern California, Department of EE-Systems, 1989.
[10] R. MacGregor, "A deductive pattern matcher," inProc. AAAI 88, Nat. Conf. Artificial Intelligence, 1988.
[11] R. MacGregor, "The evolving technology of the KL-ONE family knowledge representation systems,"Workshop on Formal Aspects of Semantic Networks, Jan. 1989.
[12] M. Minsky, "A framework for representing knowledge," inThe Psychology of Computer Vision, P. H. Winston, Ed. New York: McGraw-Hill, 1975.
[13] D. I. Moldovan, W. Lee, and C. Lin, "SNAP: A marker propagation architecture for knowledge processing,"Tech. Rep. CENG 89-10, University of Southern California, Department of EE-Systems, 1989.
[14] D. I. Moldovan, W. Lee, C. Lin, S.-H. Chung, "Parallel knowledge processing on SNAP," inProc. ICPP 90, Int. Conf. Parallel Processing, 1990.
[15] B. Nebel, "Computational complexity of terminological reasoning in BACK,"Artificial Intell., vol. 34, pp. 371-383, 1988.
[16] J. G. Schmolze and T. Lipkis, "Classification in the KL-ONE knowledge representation system," inProc. IJCAI 83, Eighth Int. Joint Conf. Artificial Intelligence, 1983.
[17] M. R. Quillian, "Semantic memory," inSemantic Information Processing, M. Minsky, Ed. Cambridge, MA: MIT, 1968.
[18] W. A. Woods, "Research in knowledge representation for natural language understanding,"Annual Rep. 4785, Bolt Berbank and Newman Inc.. 1981.

Index Terms:
parallel marker-passing architecture; frame-based systems; semantic networks; knowledge representation; concept hierarchy; subsumption relationships; large knowledge base; real-time applications; classification; property retrieval; time complexities; SNAP; execution time; computational complexity; deductive databases; knowledge representation; parallel programming; query processing
J.-T. Kim, D. Moldovan, "Classification and Retrieval of Knowledge on a Parallel Marker-Passing Architecture," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 5, pp. 753-761, Oct. 1993, doi:10.1109/69.243507
Usage of this product signifies your acceptance of the Terms of Use.