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Re: [ontolog-forum] Polysemy and Subjectivity in Ontolgies - the HDBIexa

To: "'[ontolog-forum] '" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "Rich Cooper" <rich@xxxxxxxxxxxxxxxxxxxxxx>
Date: Wed, 17 Nov 2010 10:13:46 -0800
Message-id: <20101117181400.56B39138D03@xxxxxxxxxxxxxxxxx>

Hi John and Doug,


John, you posted a response to my question on 11/6 - sorry I jut got around to having time to answer it.  Please see comments below,





Rich Cooper


Rich AT EnglishLogicKernel DOT com

9 4 9 \ 5 2 5 - 5 7 1 2


John Sowa wrote:


On 11/6/2010 12:55 PM, Rich Cooper wrote:

> Then perhaps I don't understand the reasons why you define an ontology as

> monosemous.  Why don't you think a practical ontology MUST be polysemous if

> you agree with the conclusion I reached?


For many years, I have been saying that there is no such thing as one

ideal ontology of everything.  In my 1984 book, the last chapter had

the title "Limits of Conceptualization," in which I outlined the many

problems with assuming one ideal ontology.


In my 2000 book, I covered similar material in more detail in Ch 6,

which had the title "Knowledge Soup."  That was also the title of

a talk I gave in 1987, and a paper I published in 1991.  In 2004,

I wrote a longer version, "The Challenge of Knowledge Soup":




There are several points, which I have emphasized over and over

and over again in those publications and many email notes:


  1. There is no such thing as an ideal ontology with one unique

     meaning for every term.


  2. But for any system of formal reasoning, we must have one

     meaning for each term in order to avoid contradictions.


Assuming this (point 2) is true implies we cannot reason with ambiguous representations, but that is clearly not true.  Disambiguation in language only goes a short way before reaching the many, many interpretations that can be placed on a phrase such as (I'm sure you've seen this one before):


A pretty little girls school


The last time I looked at that phrase in depth, I came up with 16 distinct meanings for it, depending on the various synsets that the various words can map to.  


Yet for NLP, we MUST handle ambiguity.  For those who remember Early’s Algorithm (Jay Early) published in ACM comm. back in the seventies or so, there are ways to represent multiple meanings just as there are ways to represent multiple parses of a phrase.  


Avoiding contradictions is bad policy for NLP, IMHO.  The contradictions are what make a person’s character so complex and yet so real.  NLP requires ontologies of parallel interpretations (unless you can provide an alternative way to handle contradictions).  


  3. Therefore, we can handle requirements #1 and #2 by providing

     an open-ended number of theories (or microtheories, as Cyc

     calls them), each of which has one meaning per term.


  4. But we can have terms with same spelling, but different

     definitions (or axiomatizations) in different theories.


  5. In order to manage all that multiplicity of theories and to

     organize them in a way that enables us to find the one we need

     for any particular problem, we can arrange them in a generalization



But a generalization hierarchy, while useful, is not the only approach.  There are plenty of ways to represent concurrent interpretations and to search among them for those interpretations which can be mapped word by word, synset by synset into graphs of possible interpretations. 


Especially with relational representations using relational database technologies, a table of interpretations can be constructed with each thread having its own elaborations of a row in that table. 


  6. The complete hierarchy of all those theories would be an infinite

     lattice, and we can't implement all of them.  But any one(s) we need

     can be created by combinations and modifications of ones we have.


I’m not certain that what is needed is a hierarchy – processing of ambiguous threads need not be the same interpretation TYPE hierarchy.  As you have pointed out many times, ambiguity in language is not equivalent to ambiguity in the word meaning lattice.  Even phrases like “throw the game”, which have no meaningful physical interpretation, still have meaning in the recipient’s and sender’s heads about what “throw” means, and it isn’t heaving a mass in a direction.  


  7. When we're not sure which of the many theories with a particular

     term, say the predicate dog(x), we can select an underspecified

     theory near the top.  As we get more info, we can move to (or

     create) a more specialized theory that adds any detail we need.


I'm sure that I repeated this in about 2,376 different notes over

the past ten years.  For the record, following is the most recent

talk in which I discussed it:



    Integrating Semantic Systems


Please read slides 61 to 81.  Then bookmark it and reread it

whenever you have a question like the one above.


I just revisited (for the fourth time) slides 61 to 81.  They are excellent slides for people who are concerned about repositories and about the portability of ontologies.  But the subject I am most concerned with is just the NLP aspects, not interfacing databases of one type to those of another type by means of an ontology.


All the years of work by many people in NLP have not paid off very well.  The basic capability needed is in active voice (or even text) interactions having a natural feel to them so that training is minimal for people who work with those systems.  Cars, phones, even the Kinect sensor, have more and more leaned toward active voice interfaces, but they all lack real effectiveness.  I believe there is more at risk here than JUST LOGIC.  The actual designation of objects, structure of language utterances, and discovery of user intent require much more than JUST logic, so what else is missing for NLP to become useful as a computer interface technique?


You mentioned Cyc and its microtheories for interpreting each meaning of a word.  It doesn’t seem to have worked, in that Cyc hasn’t gotten out of the public weal, with contract work only, and only for research not for practical applications.  So there is more than LOGIC at stake here.  I am trying to characterize what is needed to build a roadmap toward full NLP.  Any suggestions on how that roadmap would have milestones would be appreciated.  







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