this article.

SNePS, the Semantic Network Processing System, has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). This paper expands on this motivation, discusses some of the system features that derived from this motivation, and presents four case studies of interactions with SNePS demonstrating some of these features. The features demonstrated in the case studies are: nonstandard connectives; the use of recursive rules; the Unique Variable Binding Rule, that says that two variables in a rule cannot be instantiated to the same term; and discussing sentences and propositions in natural language. 1 System Description SNePS, the Semantic Network Processing System [9, 15, 17], has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). It has always been the intention that a SNePSbased "knowledge base" would ultimately be built, not by a programmer or k...

SNePS is a representative semantic network. This paper shows suggestive evidence that SNePS model of information and SNePS path-based query language have similar features as current semi-structured data models and query languages. This leads us to conclude that SNePS is well-suited to deal with semi-structured data. We show some advantages and limitations of such use of SNePS.