Massively Parallel Support for Computationally Effective Recognition Queries

Matthew P. Evett, William A. Anderson, and James A. Hendler

PARKA is a frame-based knowledge representation system implemented on the Connection Machine. PARKA provides a representation language consisting of concept descriptions (frames) and binary relations on those descriptions (slots). The system is designed explicitly to provide extremely fast property inheritance inference capabilities. In particular, PARKA can perform fast "recognition" queries of the form "find all frames satisfying p property constraints" in O(d+p) time---proportional only to the depth of the knowledge base (If, B), and independent of its size. For conjunctive queries of this type, PARKA’s performance is measured in tenths of a second, even for KBs with 100,000+ frames. We show similar results for timings on the Cyc KB. Because PARKA’s run-time performance is independent of KB size, it promises to scale up to arbitrarily larger domains. With such run-time performance, we believe PARKA to be a contender for the title of "fastest knowledge representation system in the world".

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