On 5/6/14 11:29 AM, Barkmeyer, Edward J wrote:
> This is getting out of hand, as usual.
> Two points. You wrote:
>> Every logical restriction, constraint, or rule can be represented in
>controlled NLs, such as STE or many others that can be freely downloaded.
> The problem with controlled NLs is that they define the meanings of words,
>but they aren't very good at defining the meanings of constructs and
>sentences, unless they are extraordinarily restrictive. That is where
>ontologies shine. Ever since Tarski we have understood how to assign an
>interpretation to a formal sentence. And conversely, we don't really assign
>meaning to the "words" -- constants and predicates -- at all. So, I would
>have said you were right the first time. You need both: human understanding
>of the primitive terms and formal representation of the utterances.
>> UML tools are far more widely used than *any* tools that have come out of
>the AI community -- *and* FUML specifies UML with the same degree of precision
>as any linear logic.
> Weeelll... UML tools are used for many purposes, by an assortment of people
>with a wide range of modeling skills. Most of those people, if the behavior
>of the UML tool vendors is any indication, use UML to draw parts of their
>implementations, and I would bet that more than half of those are actually
>inconsistent with fUML somewhere along the way. A UML model of itself does
>not solve any application problem, and most users do not model their
>implementation in sufficient detail for the tool to generate a complete
> By comparison, an OWL model can be fed directly to an OWL reasoning engine
>that can then solve some set of application problems. OWL, like Java, is an
>implementation language. But like other AI tools, and unlike the JVM, DL
>reasoners are not Turing machines; they are not capable of implementing
>arbitrary algorithms. So, their usage is naturally much narrower. And, with
>the possible exception of Prolog, AI tools typically exhibit this
>characteristic of a well-defined algorithm for solving only a certain class of
>problems. So, UML will naturally be more widely used.
> In addition, the skills required to produce an OWL model that solves a
>problem are different from the skills required to use Java to solve the
>problem. Those Java guys are heirs to an approach to software development
>that has been able to solve real problems for 50 years, and those skills are
>therefore a part of the mainstream education in software engineering. We have
>only recently acquired the computational power to make the AI algorithms
>practical for solutions to real problems, and we are only just learning how to
>use the knowledge base models that we develop for them. In time, the AI
>technologies will eliminate a lot of applications of Java and its relatives,
>and ontology development skills will become part of the mainstream. (01)
> (When I was in school, electrical engineers rarely acquired any digital logic
>skills, because the transistor had only just been invented. Now, digital
>logic is a pre-requisite for many electrical engineering applications. The
>lesson of history is that a technology in its time changes the practice.)
> There is already a community that models OWL ontologies in UML and turns a
>crank to spit out the OWL ontologies directly, in much the same way that a
>different UML tool crank turns out Java. But at this time, it is still a very
>small community. It will grow. (04)
Yes, and there's more to come. (05)
>> -----Original Message-----
>> From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-
>> bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F Sowa
>> Sent: Tuesday, May 06, 2014 2:10 AM
>> To: ontolog-forum@xxxxxxxxxxxxxxxx
>> Subject: Re: [ontolog-forum] English number of words/concepts that cannot
>> be composed of others
>> Ed and Pat C,
>> We agree on many points. I believe that there is a way to combine tools such
>> as STE (Simplified Technical English) and COSMO with taxonomies such as
>> Schema.org. Diagrams such as UML and others have proved to be very
>> helpful, and they can be specified with the same level of precision as any
>> linear notation for logic.
>> The issues that keep us from reaching a consensus are theoretical points
>> about the nature of language and the boundaries between a controlled
>> vocabulary, a taxonomy, and a formal ontology. We could continue arguing
>> about them forever. Or we could design something useful.
>>> [Pat's] goal of "accurate semantic interoperability among databases
>>> and applications" is in fact to be attained by having effective
>>> communication among the human authors of the databases and
>> That goal is a prerequisite for everything else. Specifications at that
>> have been used for IT applications with punched card machines and
>> computer systems for over a century. Formal ontologies may help.
>> But until we replace humans, we need humanly readable spec's.
>>> The understanding of the meanings of the ontology elements will be
>>> better than that typically obtained from people sharing definitions in
>>> a controlled vocabulary because the ontology also has many logical
>>> restrictions, evident in the text or in a viewer such as Protégé
>> Every logical restriction, constraint, or rule can be represented in
>> NLs, such as STE or many others that can be freely downloaded. UML tools
>> are far more widely used than *any* tools that have come out of the AI
>> community -- *and* FUML specifies UML with the same degree of precision
>> as any linear logic.
>>> because the ontology is in a logical form suitable for reasoning...
>> That's important. But the humanly readable form is *more* important.
>> People have been using NL spec's for over a century, and they require such
>> versions. The logic-based notations are useful, but the NL forms are
>> *essential* -- diagrams are also important.
>>> by trying to focus on the necessary semantic primitives, one keeps the
>>> ontology to the minimum size that will accomplish the task. This makes
>>> it easier to learn and easier to use.
>> I have *never* objected to having a methodology based on a limited set of
>> defining elements. I have *never* objected to research such as Anna W's
>> for finding common semantics among multiple languages.
>> What I do object to are claims that any set derived from NL research can be
>> sufficiently precise -- without further refinement -- for a formal ontology.
>> accept it as a starting point for a vague, underspecified, upper level
>> *taxonomy*. But the precise, detailed reasoning is *always* at the lower
>> Doug Lenat has said that for years: the upper level has very few axioms, and
>> all the significant reasoning is done with the middle and lower levels (the
>> microtheories). I agree with him.
>>> thus far I haven't seen any suggestions for alternative means to
>>> general semantic interoperability that appear any more likely to
>>> achieve the goal of accuracy.
>> I have no objections to that methodology. I believe that the COSMO terms
>> are good as any and better than most. But I don't believe that *any* version
>> is or can be the ultimate ideal.
>>> Even though learning a common foundation ontology takes some effort
>> I would *never* start teaching people the foundation. It's much better to
>> adopt the Schema.org strategy of starting with the terms that people can
>> start using on day 1. I have no objection to telling them that there are
>> useful defining terms -- if and when they are ready to sit down and study
>> them -- but not in lesson #1, #2, or even #5.
>> In fact, Schema.org already has a large number of users. So the *best* way
>> to get them to use COSMO (or any other set of defining terms) is to show
>> that the definitions of Schema.org stated in COSMO terms are
>> (a) more readable, (b) more flexible, and (c) more precise, and *most* of all
>> (d) easier to use than the current English paragraphs.
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