Some conjectures about the representation of knowledge are listed
below. It is the belief of this author that neither proof nor
disproof of these conjectures exist in the literature today.
If you know of any proof or disproof of these conjectures please email
. Applications and implications of these conjectures will not be dealt
with in this paper.
Algorithms can be constructed to compute and generate labeled
digraphs from any natural language statements .
Algorithms can be constructed to generate logical inferences of knowledge
represented by labeled digraphs and represent that knowledge in labeled
Algorithms can be constructed to generate natural language statements
of any knowledge that has been represented by labeled digraphs.
Labeled digraphs can be efficiently represented in a digital computer.
Examples of knowledge represented by labeled digraphs:
Knowledge can be represented in natural language.
Knowledge can be represented by labeled digraphs.
Representing labeled digraphs in a computer:
An example of this is the English sentences: "An apple is a fruit" "Fruit
A more detailed example has been "provided by a six-year-old grade 1 student
to describe his knowledge of whales".
Follow this hyper link for an example of the classic problem of Oedipus
and his wife is Jocasta answering the question "Does Oedipus know his
wife is his mother?" correctly.
The sentence: "The individual referred to by employee id 85740 is named
Ora Lassila and has the email address firstname.lastname@example.org. The resource http://www.w3.org/Home/Lassila
was created by this individual." can be represented with the
diagram under this hyper link from the RDF
There are many ways to represent labeled directed graphs efficiently
in a computer.
One way is with relational databases where each directed vector of the
graph is a row in a table. The row would then contain the following
attributes: the identity of the source node, the identity of the label
node, and the identity of the object node. So the relational database
below represents the same thing as the digraph #1 above. I have choose
to rename the objects Node, Arrow, Destination node with the attributes
Verb Object respectively, and name this knowledge representation
There exist algorithms which based on this relational data can produce
the following dialogue:
Human: An apple is a fruit.
Another way to represent labeled digraphs in a computer is with the Resource
Description Framework (RDF) as recommended by the W3C.
The sentence and the digraph in example #4 above can be placed in meta
data on the Web with the following markup:
Human: Fruits are sweet.
Human: Is an apple sweet?
Please note that paper is not proposing using graphical diagrams to
represent knowledge inside of computers. Rather it is proposing that
graphical diagrams can be used by humans to picture in our minds the underlying
deep semantic relationships that are inherent in language and knowledge.
Then without loss of precision, these deep semantic relationships can be
coded inside a computer where artificial agents can have access to them.
(c) 1998,1999 by Seth Russell
Rob Kremer of The
University of Calgary for thoughtfully posting his Ph.D. Dissertation
Graphs: A Concept Map Meta-Language on the WWW
for me to find with ZurfRider
so that I could hyper link this presentation with the already existing
"web of science" .
David Longley who's dogged
insistence on comp.ai.philosophy that AI was just the "web
of science" which was just relational databases which lead me to recognize