Advanced Search
Journal search
Journal cover: Kybernetes


ISSN: 0368-492X

Online from: 1972

Subject Area: Electrical & Electronic Engineering

Content: Latest Issue | icon: RSS Latest Issue RSS | Previous Issues

Options: To add Favourites and Table of Contents Alerts please take a Emerald profile

Previous article.Icon: Print.Table of Contents.Next article.Icon: .

Bootstrapping knowledge representations: From entailment meshes via semantic nets to learning webs

Document Information:
Title:Bootstrapping knowledge representations: From entailment meshes via semantic nets to learning webs
Author(s):Francis Heylighen, (Center “Leo Apostel”, Free University of Brussels, Brussels, Belgium)
Citation:Francis Heylighen, (2001) "Bootstrapping knowledge representations: From entailment meshes via semantic nets to learning webs", Kybernetes, Vol. 30 Iss: 5/6, pp.691 - 725
Keywords:Artificial intelligence, Cybernetics, Education, Learning, Networks, Systems
Article type:Research paper
DOI:10.1108/EUM0000000005695 (Permanent URL)
Publisher:MCB UP Ltd
Abstract:The symbol-based epistemology used in artificial intelligence is contrasted with the constructivist, coherence epistemology promoted by cybernetics. The latter leads to bootstrapping knowledge representations, in which different parts of the system mutually support each other. Gordon Pask’s entailment meshes are reviewed as a basic application of this approach, and then extended to entailment nets: directed graphs governed by the “bootstrapping axiom”, determining which concepts are to be distinguished or merged. This allows a constant restructuring of the conceptual network. Semantic networks and frame-like representations can be expressed in this scheme by introducing a basic ontology of node and link types. Entailment nets are then generalized to associative networks with weighted links. Learning algorithms are presented which can adapt the link strengths, based on the frequency with which links are selected by hypertext users. It is argued that such bootstrapping methods can be applied to make the World Wide Web more intelligent, allowing it to self-organize and support inferences.

Fulltext Options:



Existing customers: login
to access this document


- Forgot password?
- Athens/Institutional login



Downloadable; Printable; Owned
HTML, PDF (646kb)Purchase

To purchase this item please login or register.


- Forgot password?

Recommend to your librarian

Complete and print this form to request this document from your librarian

Marked list

Bookmark & share

Reprints & permissions