JS: "There is a fundamental problem about evaluating new ideas of any kind:
People always interpret new information in terms of their previously
established mental patterns and structures."
I agree with your reasoning here. As long as science exists, there has been
always an intellectual tension between the paradigms, as the established
positions of some field at a ceratin period, and the forefront, cutting edge
ideas, really progressing science and technology on. There have been always
the conservative establishment, resisting to innovation, and those commited
to novel vanguard knowledge. These groups are irreconcilable opposites, and
i doubt that such conflict of opposites could motivate the advancement of
forefront metadisciplinary knowledge and its applications. For the situation
reminds me the opposite logical positions: the closed world assumption, what
is unknown is false, and the open world presumption, what is unknown is
neither false nor true, just unknown and need research; the world is open to
new discovery . What i like with SW languages they at least based on the
[AA: I incline to think that the "crowd intelligence" or "foolishness of the
crowd" may explain the nature of the "phenomenon", and a canonic world model
encoded as a machine-understandable common ontology standard of meanings may
allow to head off it at all.]
JS: Perhaps. But my greatest fear is that the choice of standard is more
likely to be determined by the latest fad or by the organization that has
the most hype and money to throw at it. To avoid embarrassing the guilty, I
won't mention specific examples. But for anybody who advocates a common
standard ontology, I would say
Be careful what you wish for.
If any readers think that they have an ideal ontology in mind, I'd like to
ask one question: Do you believe that you have sufficient hype and money to
make your preference become the new mainstream? (01)
You know well the "inventor's paradox" from Polya's How to Solve It, general
heuristics for problem solving of all sorts and kinds: (03)
"Try solving a more general problem first; for the more ambitious plan may
have more chances of success". (04)
If mathematics is the cheapest science, ontology is tending to become one of
the most expensive sciences, so needing firm fundamental theories. There are
currently a large number of ontology-dependent research projects, costing
hundreds millions euros, like as: (05)
1. FP6 projects: ICT technologies, Knowledge and Content Technologies,
Semantic foundations, 20 projects, such as: ALVIS,..., MESH,...,
NeOn,...,TripCom, http://cordis.europa.eu/ist/kct/fp6_projects_semantic.htm (06)
2. FP 7 projects, ICT technologies, 15 projects, such as: ACTIVE,...,
LaRK,..., OKKAM, ONTORULE,..., WeKnowIt,
The solutions promised to be delivered (or had been promised to have been
delivered) are/were: open networked ontologies, SW services infrastructure,
generic semantic reference infrastructure, semantic architecture,
semantic-based search engines, integration of heterogeneous data sources,
mashups, knowledge content objects, argumentation systems, knowledge-based
business intelligence, even the large knowledge collider. (08)
Even granted that all the teams composed of brilliant individual minds, do
you really believe that any of these high tasks could be obtained without
having solid ontological and semantic foundations, for which a common
ontology of meanings is designed for? Thanks a lot. (09)
Azamat Abdoullaev (010)
----- Original Message -----
From: "John F. Sowa" <sowa@xxxxxxxxxxx>
To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
Cc: "Mustafa Jarrar" <mjarrar@xxxxxxxxxxxx>; <jeremy@xxxxxxxxxxxxxxx>;
"'SW-forum'" <semantic-web@xxxxxx>; "Sören Auer"
<auer@xxxxxxxxxxxxxxxxxxxxxxxxx>; "Pieter De Leenheer" <pdeleenh@xxxxxxxxx>
Sent: Sunday, May 10, 2009 6:49 PM
Subject: Re: [ontolog-forum] Research Illusion (011)
> There is a fundamental problem about evaluating new ideas of any
> kind: People always interpret new information in terms of their
> previously established mental patterns and structures.
> That point has several implications:
> 1. Anything that fits previously established patterns will be
> quickly perceived, interpreted, accepted, and added to the
> old patterns.
> 2. Anything that doesn't fit the old patterns will be "anomalous".
> It won't fit, it will create "cognitive dissonance", and it
> will be ignored or rejected.
> 3. Even worse than outright rejection is the misinterpretation
> caused by forcing the new information into some older
> pattern that is inappropriate and misleading.
> This is true of all kinds of learning from infancy to the most
> sophisticated scientific research. One of my colleagues at IBM
> submitted a paper to a conference, and one of the reviewers
> rejected it with the comment "I never saw anybody do anything
> like that before." Apparently, they wanted new research, but
> only if it fit the old paradigms.
> Eventually, the author managed to get the paper accepted by
> different reviewers, and the paper became a minor classic of
> its kind. This is just one of many examples of "reviewer roulette",
> which has plagued every branch of science and engineering. For
> the humanities, the problem is even worse because the criteria
> for testing ideas by experiment are much harder to apply.
> The same kinds of prejudices plague entire fields, not just
> individual reviewers. During the 1970s and '80s, another colleague
> at IBM, Fred Jelinek, was the manager of a group that used statistics
> to analyze natural languages. In those days, the amount of data
> they had to process was so large that they swamped a large IBM
> mainframe. So they had to run their programs at 3 o'clock in
> the morning, when they could get enough computing power.
> I remember that one of the researchers who worked for Fred had
> developed a parser that used statistics to guide the choice of
> which option to follow. She submitted the paper to IJCAI in 1981,
> and it was rejected with the statement "Statistics is not AI."
> By the 1990s, personal computers were as powerful as the mainframes
> of the early 1980s. So the same kinds of techniques could be run
> on PCs, and Fred Jelinek became a guru instead of a crank. Now,
> statistics is the so-called "mainstream", and papers are often
> rejected if they don't use statistics.
> Some comments on earlier comments:
>> SA: "I have the vision that research communities' crowd intelligence
>> be employed in the Web 2.0 style for deciding about research funding".
>> MB: "...we see people can vote resources...Allowing people to add
>> ontology-based annotations is just similar and would be another step
>> JC: "Google scholar provides citation counts, which while still a fairly
>> rough measure, does include an idea of the importance of any piece of
>> PDeL: "I agree with the value of the wisdom of the crowd effect in many
>> cases, however it should be controlled somehow to prevent the emergence
>> "foolishness of the crowd".
>> MP: "We second the idea of common standard ontologies for the semantic
> All of those techniques can be helpful, but none of them are magic.
> They still won't overcome the fads of the "mainstream", and they still
> can't distinguish a truly significant innovation from the latest fad
> or somebody's pet idea "that just ain't so".
>> AA: I incline to think that the "crowd intelligence" or "foolishness of
>> crowd" may explain the nature of the "phenomenon", and a canonic world
>> encoded as a machine-understandable common ontology standard of meanings
>> may allow to head off it at all.
> Perhaps. But my greatest fear is that the choice of standard is more
> likely to be determined by the latest fad or by the organization that
> has the most hype and money to throw at it. To avoid embarrassing
> the guilty, I won't mention specific examples. But for anybody who
> advocates a common standard ontology, I would say
> Be careful what you wish for.
> If any readers think that they have an ideal ontology in mind, I'd
> like to ask one question: Do you believe that you have sufficient
> hype and money to make your preference become the new mainstream?
> John Sowa
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