Information Technology
On the epistemological status of semantic networks
This paper examines in detail the history of a set of network-structured formalisms for knowledge representation - the so-called semantic networks. While these nets have for the most part retained their basic associative nature, their primitive representational elements have differed significantly from one project to the next. These differences in underlying primitives are symptomatic of deeper philosophical disparities, and a set of five significantly different levels at which networks can be understood are discussed. One of these levels, the epistemological, or knowledge-structuring, level, has played an important implicit part in all previous notations, and is here made explicit in a way that allows a new type of network formalism to be specified. This new type of formalism accounts precisely for operations like individuation of description, internal concept structure in terms of roles and interrelations between them, and structured inheritance.
Cooperative responses from a portable natural language data base query system
Doctoral dissertation, Dept. of Computer and Information Science, University of Pennsylvania. Preview only. Later published in Artificial Intelligence, Volume 19, Issue 2, October 1982, Pages 165-187 (https://www.sciencedirect.com/science/article/abs/pii/0004370282900352).
Semantic network representations in rule-based inference systems
Duda, R. O. | Hart, P. E. | Nilsson, N. J. | Sutherland, G. L.
"Rule-based inference systems allow judgmental knowledge about a specific problem domain to be represented as a collection of discrete rules. Each rule states that if certain premises are known, then certain conclusions can be inferred. An important design issue concerns the representational form for the premises and conclusions of the rules. We describe a rule-based system that uses a partitioned semantic network representation for the premises and conclusions." In D. A. Waterman and Frederick Hayes-Roth. 1978. Pattern-Directed Inference Systems. Academic Press, Inc., Orlando, FL, USA. pp. 203-221.
Synthesizing constraint expressions
See also: Communications of the ACM 21 (11):958-966
Programming in the interactive environment: The LISP experience
This language must satisfy minimal criteria, as follows.
Optimizing decision trees through heuristically guided search
CACM, 21, 1025–1039.