Bradley, John, Ian, Dean et al., (01)
While this OWL related conversation is interesting, it is actually
more appropriate in the [ontolog-forum] list. I have, therefore, made
an attempt to move the conversation over to that listserv. Kindly
continue the discourse there. (02)
Thanks. =ppy (03)
p.s. We are expecting the focus of this upcoming Ontology Summit to
be on the topic of "Making the Case for Ontology." If we were to
assuming, at least, that funders/investors, technology adopters, ...
etc. are the target audience (to whom we need to make the case),
debates on fine points about technology (not that it is not important)
here may be inappropriate, and may be seemed outside of the scope.
-- (04)
On Fri, Dec 10, 2010 at 10:57 AM, John F. Sowa <sowa@xxxxxxxxxxx> wrote:
> Ian,
>
> Before saying anything else, let me emphasize that I believe the work
> on algorithms, complexity, and decidability by you and your colleagues
> is very high quality and very important for computer science.
>
> But the sentences at the end of your note explain why *I hate OWL* :
>
>> In fact, it can be shown that query answering in OWL 2 RL [Rule Language]
>> is possible in time that in the worst case increases only polynomially
>> with the size of the data. In *this* sense, OWL 2 RL really is less
>> (computationally) complex. However, as I mentioned above, the price
>> users pay for this is an *increase* in syntactic or cognitive complexity.
>
> By syntactic complexity, I realize that you are talking about something
> much more fundamental (and cognitively much harder for people to learn)
> than the angle brackets. But knowledge acquisition has always been
> the major bottleneck in AI and the SW. Anything that increases the
> "cognitive complexity" is a bad step in the wrong direction.
>
> As Dean said,
>
>> I find that in the classes I do teach, the students are very concerned
>> about complexity in the computational sense...
>
> But there are many ways of dealing with computational complexity while
> actually *reducing* the cognitive complexity:
>
> 1. Design patterns. Every programming language is undecidable, but no
> programmer would ever ask for less expressive power. Instead, they
> have developed *design patterns* for systematic ways of using their
> languages in ways that are known to be safe and efficient.
>
> 2. Hybrid systems. The original DLs were packaged as hybrids with
> the DL component designed for efficient classification and a more
> expressive language (rule-based, full FOL, or even arbitrary
> procedures) were used to achieve the required expressive power.
> And design patterns (or something similar) can be used for the
> more expressive part of the hybrid. (The RL option of OWL doesn't
> address the main reason why people use hybrids: they need more
> expressive power, not less.)
>
> 3. Dynamic algorithm selection. Cyc has developed the largest formal
> ontology on the planet, but CycL imposes no restrictions on the
> expressive power. Instead, they use dynamic methods for selecting
> appropriate algorithm(s) for each problem or subproblem they
> encounter. Similar strategies are also used for the systems that
> compete on the Thousands of Problems for Theorem Provers (tptp.org).
>
> 4. Knowledge compilers. For many applications, it's possible to do
> a *static* selection of the algorithms: Map the very expressive
> languages (such as CycL and others) via appropriate design patterns
> to forms can be processed efficiently by known algorithms.
>
> I'm sure that you know the references for these methods, but for
> other readers, I include some in the following article:
>
> http://www.jfsowa.com/pubs/fflogic.pdf
> Fads and Fallacies About Logic
>
> At the ICCS 2010 conference, Boris Motik gave a good presentation
> about adding finite graph models to OWL in order to broaden its
> expressive power while preserving decidability.
>
> I certainly like the idea of supporting graphs, but not the idea
> of adding more cognitive complexity to an already overstuffed
> language. Instead of stuffing more into OWL, why don't you ask
> some of your students to do research on methods such as #1 to #4
> above to find ways of *reducing* the cognitive complexity?
>
> Other talks at ICCS described more efficient algorithms for
> Formal Concept Analysis (FCA), which generates consistent lattices
> from source data that is cognitively extremely simple.
>
> That would be another excellent topic for your students: design
> hybrid systems that combine an FCA-style of hierarchy with automated
> or semi-automated methods for supporting additional expressive power
> at varying levels of complexity up to the level of CycL.
>
> Cognitive complexity is killing the Semantic Web. As a result,
> people are building their own hybrids that add very scruffy methods
> to OWL or RDFS or RDFa -- thereby destroying the decidability that
> the OWL restrictions were designed to support.
>
> The four techniques above (or something similar) would be an
> excellent way to support Tim B-L's project for "Web Science".
>
> John
>
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