The connection machine
Computational complexity of terminological reasoning in BACK
Artificial Intelligence
Integrating Marker Passing and Problem Solving: A Spreading Activation Approach to Improved Choice in Planning
Application of Spreading Activation Techniques in InformationRetrieval
Artificial Intelligence Review
Developing federation object models using ontologies
WSC '04 Proceedings of the 36th conference on Winter simulation
Spatial hardware implementation for sparse graph algorithms in GraphStep
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Marker-Passing inference in the scone knowledge-base system
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
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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.