Woods, William A.


Meaning and Links

AI Magazine

This article presents some fundamental ideas about representing knowledge and dealing with meaning in computer representations. I will describe the issues as I currently understand them and describe how they came about, how they fit together, what problems they solve, and some of the things that the resulting framework can do. The ideas apply not just to graph-structured "node-and-link" representations, sometimes called semantic networks, but also to representations referred to variously as frames with slots, entities with relationships, objects with attributes, tables with columns, and records with fields and to the classes and variables of object-oriented data structures. After that, I will present some of the key ideas from that paper with a discussion of how some of those ideas have matured since then.


What’s in a link? Foundations for semantic networks

Classics

Abstract: The paper is concerned with the theoretical underpinnings for semantic network representations. It is concerned specifically with understanding the semantics of the semantic network structures themselves, i.e., with what the notations and structures used in a semantic network can mean, and with interpretations of what these links mean that will be logically adequate to the job of representing knowledge. It focuses on several issues: the meaning of'semantics', the need for explicit understanding of the intended meanings for various types of arcs and links, the need for careful thought in choosing conventions for representing facts as assemblages of arcs and nodes, and several specific difficult problems in knowledge representation - especially problems of relative clauses and quantification.



Transition Network Grammars for Natural Language Analysis

Classics

Full text available for a fee."The use of augmented transition network grammars for the analysis of natural language sentences is described. Structure-building actions associated with the arcs of the grammar network allow for the reordering, restructuring, and copying of constituents necessary to produce deep-structure representations of the type normally obtained from a transformational analysis, and conditions on the arcs allow for a powerful selectivity which can rule out meaningless analyses and take advantage of semantic information to guide the parsing. The advantages of this model for natural language analysis are discussed in detail and illustrated by examples. An implementation of an experimental parsing system for transition network grammars is briefly described."Communications of the ACM, Vol. 13, No. 10, October, 1970, pp. 591-606 (reprinted in RNLP: 71-88)