From: "Richard H. McCullough" To: "RDF-Interest" Cc: "William Thomas" , "Richard S. Latimer" Bcc: "Rhonda Cates" , "Sheila M. Faber" , "Virginia McCullough" , "Deborah G. Cates" , "Steven V. Cates" , "Theodore R. McCullough" , "Robert B. McCullough" Subject: my context Date: Tue, 26 Nov 2002 22:12:04 -0800 Organization: retired from Bell Labs MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_NextPart_000_0007_01C29598.D9BB8CF0" X-Priority: 3 X-MSMail-Priority: Normal X-Mailer: Microsoft Outlook Express 6.00.2800.1106 X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2800.1106 This is a multi-part message in MIME format. ------=_NextPart_000_0007_01C29598.D9BB8CF0 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable # KEHOME/knowledge/theory/Epistemology/MyContext.txt # Nov/26/2002 #=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D# # My Context # #=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D# Suppose I make the statement I saw Bob Hope in person at the Presidio. What is the context of my statement? Quite literally, it is everything I know. at space=3Dhere, time=3Dnow, view=3DDick McCullough knows { I saw Bob Hope in person at the Presidio } How can you understand what I said? Because your context is everything you know, and our contexts have a lot in common. at space=3Dthere, time=3Dnow, view=3Dyou know { Dick McCullough saw Bob Hope in person at the Presidio } Note: For this example, I'm leaving out the extra layer of context at view=3DDick McCullough says { ... } From the viewpoint of knowledge representation, capturing everything I know is a difficult problem. What can we do to simplify that problem? I see two promising approaches. 1. We can use genus-differentia definitions to condense the knowledge. at view=3DDick McCullough definitions { ... } 2. We can select only those definitions that are relevant to the words in my statement. at view=3Drelevant Dick McCullough definitions { ... } In theory, these are common-sense, reasonable approaches; in practice, they need to be tested. =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=20 Dick McCullough=20 knowledge :=3D man do identify od existent done knowledge haspart list of proposition ------=_NextPart_000_0007_01C29598.D9BB8CF0 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable
#=20 KEHOME/knowledge/theory/Epistemology/MyContext.txt
# = Nov/26/2002
 
#=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D#
# My Context=20 #
#=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D#
 
Suppose I make the = statement
 
    I saw Bob Hope in = person at the=20 Presidio.
 
What is the context of my = statement?  Quite=20 literally,
it is everything I know.
 
    at space=3Dhere, = time=3Dnow,=20 view=3DDick McCullough knows = {
        I saw=20 Bob Hope in person at the Presidio
    }
 
How can you understand what I = said?
Because your=20 context is everything you know,
and our contexts have a lot in=20 common.
 
    at space=3Dthere, = time=3Dnow,=20 view=3Dyou know {
        Dick = McCullough=20 saw Bob Hope in person at the Presidio
    = }
 
Note:
For this example, I'm leaving = out the=20 extra layer of context
 
    at view=3DDick = McCullough says {=20 ... }
 

From the viewpoint of knowledge representation, = capturing
everything=20 I know is a difficult problem.  What can we do
to simplify that=20 problem?  I see two promising approaches.
 
1. We can use genus-differentia definitions to condense=20 the
knowledge.
 
    at view=3DDick McCullough definitions { ... = }

2. We=20 can select only those definitions that are relevant to
the words in = my=20 statement.
 
    at view=3Drelevant Dick McCullough definitions { = ...=20 }
 
In theory, these are common-sense, reasonable approaches;
in = practice,=20 they need to be tested.
=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D =
Dick McCullough
knowledge :=3D man do = identify=20 od existent done
knowledge haspart list = of=20 proposition
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