Processing of semantic nets on dataflow architectures

✍ Scribed by Lubomir Bic

• Publisher: Elsevier Science
• Year: 1985
• Tongue: English
• Weight: 436 KB
• Volume: 27
• Category: Article
• ISSN: 0004-3702
• DOI: 10.1016/0004-3702(85)90054-2

✦ Synopsis

Extracting knowledge from a semantic network may be viewed as" a process of finding given patterns in the network. On avon 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 perJorm 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.