Dear Wolfgang, Martin, and all,
My Edge contribution does not put into question the CIDOC-CRM, or the
hermeneutic circle in general. The argument is that quantitative
measurement accelerates the process and extends beyond the reach of
qualitative inquiry.
For a more extended account with figures, please take a look at
Maximilian Schich: Figuring out Art History. DAH-Journal 2 (2016) [to
appear]
Preprint: http://arxiv.org/abs/1512.03301 (22 Oct 2015)
Please let me also answer your questions briefly...
Martin's questions:
* Like glaciers, frozen models still move and are often beautiful and
vital as a "reference". ;)
* The initial hypotheses may be established in a traditional way:
Facebook for example has updated their traditional gender-model from
male/female to a list of 250 genders, emerging from user input,
characterized by a frequency distribution and temporal dynamics that
can only be measured in a quantitative way. The emerging ENRON email
structure versus ENRON's defined corporate hierarchy is another good
example for the need of quantification.
* Yes, after measurement, disproving hypotheses, and finding a way out
of Uri Alon's "cloud of uncertainty" (aka science), the loop needs
to be closed (by engineering). Therefore, the CRM-SIG will be even
more important than before (doing both science and engineering).
* I use the word "measurement" in the sense of Max Planck, who claimed
that any observation of the "real world" is subject to measurement
bias, either due to imperfections in our tools or our own sensory
organs.
* Quantity is not indicating quality per se. But quantification can
reveal hidden quality as "more is different".
* There is no confusion: Cultural research is part of the cultural
process itself.
* Yes, physicists are aiming to improve physics by studying
interaction patterns between physicists => Sinatra et al. Nature
Physics 11, 791-796 (2015) doi:10.1038/nphys3494 (cf. final paragraph)
Wolfgang's questions:
* Of course, data comes from databases old and new. In fact, analyzing
old datasets most interesting, as their data models were usually
formulated decades ago, without knowing the emerging complex
patterns that result from "local activity" by curators and the
heterogeneity of granular data collected over time.
* The data and method of our Science paper is published in the
Supporting Online Material (free access to the Science website via
www.cultsci.net). This allows for reuse and feedback of conceptual
ideas by others. I assume all steps of the hypercycle will have
their own publication stream, feeding into following steps.
* Our conceptual reference models are out of sync with (a) a large
number of databases with tens of thousands of entity and property
types, and (b) massive amounts of data where the entire ontological
structure is hidden (for example in plain tagging/category systems).
In both cases, quantification is essential to model the emerging
structure and dynamics, and eventually update our conceptual models.
In sum, all stages of the hermeneutic hypercycle are essential.
Quantification will play an important part. But this does not mean
traditional ontology engineering will go away.
Best regards,
Max
*Dr. Maximilian Schich*
Associate Professor, Arts & Technology
Founding member, The Edith O'Donnell Institute of Art History
*/The University of Texas at Dallas/*
800 West Campbell Road, AT10
Richardson, Texas 75080 – USA
US phone: +1-214-673-3051
EU phone: +49-179-667-8041
www.utdallas.edu/atec/schich/ <http://www.utdallas.edu/atec/schich/>
www.schich.info <http://www.schich.info>
www.cultsci.net <http://www.cultsci.net>
Current location: Dallas, Texas
On 2016-01-08 8:58 , martin wrote:
Dear All,
Just to add to Wolfgang's remark:
The CRM is in no point a product of a priori intuition, but
exclusively based on empirical study of
database use and interpretation, and a continuous feed back to
systematic updates of the CRM.
More flexible mapping mechanisms and semantic Web technologies also
enable the systematic update
of the databases to new releases. The CRM, as ISO standard, is not
"frozen", but has the regular update
cycle of 5 years, which CRM SIG extensively uses.
How ontological relations can emerge from quantitative measurements is
black magic to me:
All quantitative measurement requires an a priori hypotheses, and
competing hypotheses will reveal better
or worse agreement with reality. So far sciences appear to me to work.
So, what are the initial hypotheses
about such patterns? Or is there again an ontology engineering step
after the measurement?
I agree that the real ontological patterns are often not what expert
intuition would suggest in the first place.
This is our common experience. However once found to be operational,
they must be compatible with
scientific argumentation and expert can confirm. I agree with
Maximilian that data structures must be based on
empirical research, but "measurement"?
The "quantitative" argument is equally puzzling to me. Is quantity now
indicating quality? Aren't we
here confusing the sociology of doing cultural research and the
evolution of knowledge with nature
of the subject matter and the structure and logic of the scholarly
argument? Would anybody reasonably
try to improve the science of physics by studying interaction patterns
between physicists???
All the best,
martin
On 8/1/2016 9:12 πμ, Wolfgang Schmidle wrote:
Dear All,
Let me quote from fellow list member Maximilian Schich's critique of
database models and CRM:
"Over decades, database models, to embody the underlying worldview,
were mostly established using formal logic and a priori expert
intuition. Database curators were subsequently used to collect vast
numbers of specific observations, enabling further traditional
research, while failing to feed back systematic updates into the
underlying database models.
As a consequence, "conceptual reference models" are frozen, sometimes
as ISO standards, and out of sync with the non-intuitive complex
patterns that would emerge from large numbers of specific
observations by quantitative measurement. A systematic data science
of art and culture is now closing the loop using quantification,
computation, and visualization in addition."
http://edge.org/response-detail/26784
Max, let me start by asking where the data underlying visualisations
such as https://www.youtube.com/watch?v=4gIhRkCcD4U is supposed to
come from, if not an old-fashioned database? How did you feed the
"non-intuitive complex patterns" emerging in this visualisation back
into the database or somewhere else? And why do you think CRM is out
of snyc with this?
Thanks
Wolfgang
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