The obverse of Michael's good point is that we must acknowledge and muffle instances of 'bad' ontologies. In the world of innovation this means that we must not only address why X is important but also how to recognize X from Not X. This is best done by formulating a value proposition. Bothering users about how to build a watch is counterproductive, however, the marketplace also consists of distribution channels and ontology education sources so a separate value proposition for them will be useful, too. On Dec 14, 2010, at 12:18 PM, Michael F Uschold wrote: Responding both Nicola's and Aldo's comments.
We need to walk before we can run. At at time when most people don't get the basic value of having an ontology in the first place, any efforts spend on arguing why you need a better ontology, or why ontology design is important is wasted.
By analogy, before people got the idea of a cell phone, spending time marketing the importance of building better cell phones is silly. It is putting the cart before the horse. The cart naturally follows the horse when the horse gets going.
When people get the value of ontologies, they will naturally get the importance of building good ontologies.
For any new technology that is not widespread, you don't focus marketing efforts on why on building a better X is important, you focus on why having X is good in the first place.
Michael
Michael
On Tue, Dec 14, 2010 at 7:27 AM, Aldo Gangemi <gangemi@xxxxxxx> wrote:
Hi, indeed the discussion on OWL specifically is more appropriate to the forum list.
However, John's message is very relevant for making the case we want to make more solid.
As a matter of fact, "ontology" as a field studying good design of multiple ontologies aka vocabularies aka schemas could be a good selling point, *but* specially if it helps reducing complexity, be it cognitive or computational, or even social (cf. sustainability of semantic applications in a large organization).
The design pattern approach (as e.g. the portal at [1] is supporting) aims at that goal primarily, covering good practices that span across content modelling, logical issues, reasoning recipes, reengineering procedures, etc..
Maybe we can rephrase the title as "Making the case for ontology design", if that does not hurt too much the supporters of the "one and real ontology" (which might be one of the sources of good practices). Strange as you may think, ontology design is not yet "obvious" and quietly accepted when discussing semantic applications, let alone a vague "ontology" discipline ;)
Aldo
On 10 Dec 2010, at 20:24, Peter Yim wrote:
Bradley, John, Ian, Dean et al., 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. Thanks. =ppy 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. --
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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Fax: +390644161513
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