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<p>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.</p>
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

D. Moldovan and J. Kim, "Classification and Retrieval of Knowledge on a Parallel Marker-Passing Architecture," in IEEE Transactions on Knowledge & Data Engineering, vol. 5, no. , pp. 753-761, 1993.
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