Using the Parka Parallel Knowledge Representation System (Version 3.2)

Abstract

Parka is a symbolic, semantic network knowledge representation system that takes advantage of the massive parallelism of supercomputers such as the Connection Machine. The Parka language has many of the features of traditional semantic net/frame-based knowledge representation languages but also supports several kinds of rapid parallel inference mechanisms that scale to large knowledge-bases of hundreds of thousands of frames or more. Parka is intended for general-purpose use and has been used thus far to support A.I. systems for case-based reasoning and data mining. This document is a user manual for the current version of Parka, version 3.2. It describes the Parka language and presents some examples of knowledge representation using Parka. Details about the parallel algorithms, implementation, and empirical results are presented elsewhere. (Also cross-referenced as UMIACS-TR-95-68)