Processing of semantic nets on dataflow architectures

摘要

Extracting knowledge from a semantic network may be viewed as a process of finding given patterns in the network. On a von Neumann computer architecture the semantic net is a passive data structure stored in memory and manipulated by a program. This paper demonstrates that by adopting a data-driven model of computation the necessary pattern-matching process may be carried out on a highly-parallel dataflow architecture. The model is based on the idea of representing the semantic network as a dataflow graph in which each node is an active element capable of accepting, processing, and emitting data tokens traveling asynchronously along the network arcs. These tokens are used to perform a parallel search for the given patterns. Since no centralized control is required to guide and supervise the token flow, the model is capable of exploiting a computer architecture consisting of large numbers of independent processing elements.