ORB Visualization
(soon)
3/23/2004 9:29 AM
The “glass bead game” process we are using involves the development of an Ontology referential base (Orb) designating subject matter that has a simple linguistic variation in the expression of human thought as text. The measurement described in the notational paper is a simple one and can be made more sophisticated using rapidly developed linguistic and knowledge ontology resources.
The neuro-cognitive foundation to this work is suggested in various chapters in an on-line (Open Source) book.
Foundations for Knowledge
Science
The mathematical/logic foundations are addressed in various published and unpublished materials I have produced and which has been developed by others.
Part of the task related to the establishment of a knowledge technology is to bring this work forward into a coherent literature, including experimental literature on observed differences between a stratified knowledge base supporting differential and formative ontology and systems like Lenat’s or Protégée.
In reading the short pdf “What is the “Context” for Contextual Vocabulary Acquisition?” I was struck by several observations.
“Contextual vocabulary acquisition is the active
deliberate acquisition of a meaning for a word in a text by reasoning from
textual clues and prior knowledge, including language knowledge and hypotheses
developed from prior encounters with the word,.. (first paragraph)
“Almost everyone working on this topic believes
that it is possible to “figure out” a meaning for a word “from context”. Other terms in the literature include “construct”,
“deduce”, “derive”, “educe”, “guess”, “infer”, or “predict”; I prefer to say
that the reader “computes” a meaning for an unknown word; that is what our
software does, and what our algorithm-based curriculum teaches. (ninth paragraph)
The paper is rich in the foundational elements of a future learning and teaching theory, and is grounded in first and second language acquisition research as well as other areas of natural science.
Many of us feel that this assumption about computers is unwarranted and actually keeps us from understanding how to properly use computers as aids to knowledge sharing.
However, leaving that issue aside, perhaps it would be good to talk about the encoding of machine representation of situational context.
In your paper you say:
A clue to the nature of context is .. we use a
knowledge-representation and reasoning system to represent both the information
in the text and the reader’s background knowledge.
For me this seems a good concept for a two-sided semantic web. The two representations can interact in various ways to push or pull information and to receive correction to the representation, or updates.
The Orb work and the Tri-level architecture are consistent with this insight of having two representations.
For me, the need that has lead me to propose a National Project to establish the knowledge sciences, is that this type of work is advanced with some much difficulty, not because of the absence of good science or insightful innovations.