Abstract
A central focus of this book has been on the development of a new architecture for semantic memory that provides a framework for addressing the “background-knowledge” problem: how can the background frame knowledge be represented in a machine? I have argued for using two levels of semantic memory to encode this knowledge: (1) a relational level to encode the lawful and systematic associations, and (2) an associational level to encode the background knowledge associated with the concepts that participate in these systematic associations. The book thus stakes out a middle ground arguing that both relational (systematic) and non-relational (nonsystematic) knowledge be represented in memory to foster comprehension.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer Science+Business Media New York
About this chapter
Cite this chapter
Bookman, L.A. (1994). Conclusions. In: Trajectories through Knowledge Space. The Springer International Series in Engineering and Computer Science, vol 286. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-2780-0_9
Download citation
DOI: https://doi.org/10.1007/978-1-4615-2780-0_9
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-6201-2
Online ISBN: 978-1-4615-2780-0
eBook Packages: Springer Book Archive