On Tue, 2012-02-21 at 10:28 -0500, John F. Sowa wrote:
> Leo, Hans, and Rich,
> We all agree that common semantics is a prerequisite for any kind of
> communication among people and/or computers. I would also add that
> we need some common semantics even when we communicate with our
> beastly friends and foes.
> Questions for this forum: How much common semantics is required?
> How should it be encoded? Is it primarily task-oriented semantics
> that changes from one transaction to another? Should it be encoded in
> a large shared general ontology? In some collection of task-oriented
> modules or microtheories? In some implicit or procedural knowledge
> encoded in procedures (for computers) or in habits (for people and
> other animals)? In some mixture of formal logic, informal natural
> languages, procedures, habits, or miscellaneous?
> And the thorniest question of all: How do we accommodate the trillions
> of dollars of legacy software in daily use for mission-critical systems
> that won't be replaced for decades to come? (01)
I have only a dim understanding of finance, but I do recall a finance
professor emphasizing the point that sunk costs are immaterial to future
spending decisions. So past investments in software are of no concern. A
rational CFO will only ask how much return he will get from the next
dollar he spends. The question comes down to putting your enterprise
intellectual property into proprietary OIDs (application-specific object
identifiers) and all the machinery it takes to make those OIDs useful,
or putting it into URIs (and RDF triples) and get much of the caring and
feeding of them for free so you can invest more of your intellectual
resources into getting the most out of your cool URIs. I think it's
clear that a dollar spent on URIs will benefit you more than a dollar
spent on OIDs (for any but the most ephemeral data). And because of the
multiplier power of linked data, you will obtain increasing marginal
returns from future dollars spent on URIs. (02)
The enterprise data that is currently locked up in OIDs must of course
be put into URIs and triples, but that is bread-and-butter work (after
the highly demanding work of creating the target model). Then there is
certainly some valuable functionality the software vendor has provided
in exchange for locking up your data. That is an opportunity for
software-as-a-service providers to step up and build equivalent
functionality for open data sets. (03)
> Recommendation: Instead of developing "proactive" standards for
> ontology, I suggest that we note the law of standards: examine
> what actually works, harmonize the best practices, and build the
> new additions as extensions to the de facto standards. (05)
I'm reminded of the Dickens Pickwickian who thought he would write a
paper on "Chinese Philosophy" by reading in the encylopedia under
"China", and then under "Philosophy", and combine the two. I don't know
how much harmony we would find by reading under "Description Logics",
"ISO 10303", "ISO 15926", "SysML", and other nominees for best
I agree it is normally not prudent to get ahead of established practice.
But I don't consider much of past practice (in the IT realm) to be worth
saving. Keep parametric solid modeling and discard the rest of IT's
contribution to product and systems engineering, and we'll be better off
because then we could see clearly. We could take a fresh look at what
the designers, builders, and users of systems actually *think* and *do*
(and *why* they do those things), and maybe we could do some good for
> Source: http://www.jfsowa.com/talks/iss.pdf
> Slide 4:
> Size of the Problem
> Some estimates:
> ● World-wide digital data in 2009: 800 million terabytes.
> ● Estimated digital data in 2010: 1.2 billion terabytes.
> ● Legacy software: Half a trillion lines of code.
> ● Percentage that is tagged with semantics: Slightly over 0%
> Slide 7:
> How Could Semantics Help?
> Typical programmer productivity with current tools:
> ● 10 to 15 lines of fully debugged code per person per day.
> ● Cost per line of code: $18 to $45.
> ● Most of the time is spent on analysis, design, and testing.
> ● Specification errors are the most costly and time consuming
> and the most likely to benefit from clearly defined semantics.
> Why semantic technology isn’t used more widely:
> ● Too much time, effort, and training to specify semantics.
> ● Delayed implementation without any obvious benefit.
> ● Difficulty of sharing semantics among different tools,
> especially tools designed for different methodologies.
> We need better tools and better integration among tools. (08)
Careful here. If by "better" you mean "easier for uninformed and
inexperienced people to use", you just help people do dumb stuff faster
(think word processors, HTML editors). (09)
If by "better" you mean tools that make it easier to do the right sorts
of things, and harder to do the wrong sorts of things, then I'm all for
it. (It's up to the Quality track to figure out the right and wrong
sorts of things in ontologies.) (010)
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