title: Bootstrapping knowledge representations: from entailment meshes via semantic nets to learning webs
creator: Heylighen, Francis
subject: Applied Cognitive Psychology
subject: Artificial Intelligence
subject: Neural Nets
description: The symbol-based, correspondence epistemology used in AI is contrasted with the constructivist, coherence epistemology promoted by cybernetics. The latter leads to bootstrapping knowledge representations, in which different parts of the cognitive system mutually support each other. Gordon Pask's entailment meshes and their implementation in the ThoughtSticker program are reviewed as a basic application of this methodology. Entailment meshes are then extended to entailment nets: directed graph representations governed by the "bootstrapping axiom", determining which concepts are to be distinguished or merged. This allows a constant restructuring and elicitation of the conceptual network. Semantic networks and frame-like representations with inheritance can be expressed in this very general scheme by introducing a basic ontology of node and link types. Entailment nets are then generalized to associative nets characterized by weighted links. Learning algorithms are presented which can adapt the link strengths, based on the frequency with which links are selected by hypertext browsers. It is argued that these different bootstrapping methods could be applied to make the World-Wide Web more intelligent, by allowing it to self-organize and support inferences through spreading activation.
date: 1997
type: Preprint
type: PeerReviewed
format: text/html
identifier: http://cogprints.org/458/1/BootstrappingPask.html
format: application/postscript
identifier: http://cogprints.org/458/3/BootstrappingPask.ps
identifier:   Heylighen, Francis  (1997) Bootstrapping knowledge representations: from entailment meshes via semantic nets to learning webs.  [Preprint]     
relation: http://cogprints.org/458/