Re: [FRIAM] It's cute but what is it for? To Explore Knowledge

[Stephen Thompson]

Hi All: 

I am Stephen Thompson and relatively new to this email stream.   I 
recently completed an MS in software engineering however, I am employed 
as an investment analyst for the past 25 years.  I listen in on this 
conversation to learn interesting things.  The Map of Knowledge 
discussed in this thread connects directly to my last class in the MS 
program.  

I studied the concept of ontologies, how to construct, and then to use 
them.  With my limited understanding of them, I see this Map of 
Knowledge as at least a semantic-net if not a type of ontology.  The 
latter classification depends upon the amount and extent of the 
constraints in the structure.  Usually, ontologies start out as some 
form of hierarchical tree structure to establish the backbone of the 
knowledge to be modeled.  However, the value added are in the horizontal 
connections between concept nodes.  These lateral-links embody knowledge 
and provide a way to explore the subject that can lead to new 
understanding.  Creating the ontology allows such exploration in an 
easier manner than the old-fashioned way of opening up 50 some books in 
a library, laying them out on adjacent tables, and then walking around 
reading and making connections.  (as long as you don't mutter out loud 
the librarians will leave you alone). 

This particular Map of Knowledge appears to be created by modeling users 
of scientific literature and the collective connections they followed 
through the literature.   So this is a picture of the explorations of a 
set of scientists through a sub-set of published research.   In a manner 
of speaking this is a visual representation of how those scientists 
think.   If the nodes contained information and the links were labeled, 
the semantic-net could be queried to provide interesting information 
about current explorations in science.  (by this sub-set of people)

The observations made by Steve Smith are just the kind of questions to 
pose to this semantic-net that would lead to information about the type 
of inquiry going on among this subset of scientists.   It could even 
lead to discovery of areas that are not being explored and the resulting 
question - Why Not? Mr. Smith posed several such questions. 

I imagine the map would look different if the content of the articles 
and their references were converted to OWL (an ontology language) 
triples and then merged.  The Map would then show the state of research 
results rather than a map of the investigation-search patterns of those 
using this body of online journals.   It would also look different if a 
collection of standard textbooks from each of the areas of discipline 
were converted to an ontological language and then merged.   Then we 
would be looking at the accepted state of knowledge of those fields, 
collectively, at the point in time of the source text publications.  

******
What would a map of the FRIAM email forum look like over one-two year 
period?   I bet it would be fun to look at.  
******

Dipping back into creativity techniques I used to teach my elementary 
students in the early 1980s, a map of this type serves as a source of 
rich creative ideas.  Due to the nature of this body of knowledge, most 
rich for trained scientists who understand the information contained in 
the original journals.    But imagine it was a map of knowledge you 
understood.  A simple technique of taking two unconnected nodes, looking 
at the content of each of the nodes, and then think how one can make 
connections between the two nodes based on any number of techniques:  
same word used with different meanings, homonyms, some connection 
generated by a comparison of a picture each node generates in your mind, 
etc. 

A contrived example could be like the following.  I am manufacturing 
this example from a Wall Street article published in June 1986, center 
column, front page.  That article was a human interest story of a man 
who made lists of everything and while he didn't have the high test 
training of a PhD. he did move in the same circles with MIT computer 
scientists.   His creative technique was to combine multiple lists as 
strings of keywords and then generate questions or ideas from the random 
combinations.  One such result list contained information on medical 
concepts and in the article a doctor saw one combination of blush and 
arteries.  The article describes him pondering these two words and he 
concluded that it might be a fruitful area of research. 

So for the Map under review here two unconnected nodes concerning 
arteries (physiology) and blushing (psychology) might be connected 
(creatively) to result:  "Do arteries blush?"  So I wonder if such 
connections might also be made from semantic-nets such as the Map of 
Knowledge considered in this threaded conversation. 

In summary, two possible uses for such Maps of Knowledge are to explore 
the rich interactions between connected nodes (analysis) and use the Map 
as raw material to make new connections between different areas of the 
Map (creativity). 

Steph T

PS I dream of 3-D representations of such maps poised over a table that 
can be rotated in a manner similar to the star-field projections used by 
Golan Trevize as he traveled about the galaxy in Asimov's Foundation and 
Earth.




Steve Smith wrote:

Map of knowledge 
at http://www.nytimes.com/2009/03/16/science/16visuals.html?_r=2&emc=eta1 
<http://www.nytimes.com/2009/03/16/science/16visuals.html?_r=2&emc=eta1> built 
by scientists from LANL, SFI etc.

I must admit, I have a hard time working out what these network 
visualizations are meant to be telling me. That academic disciplines 
are connected? Did I *really* not know that before looking at the 
pretty picture?
You are not alone in this observation... but I, for one,  do get a lot 
out of it, and can imagine getting a lot more if I had direct access 
to the data (and tools not unlike the one used to create this "map").

http://www.nytimes.com/imagepages/2009/03/13/science/16visual-popup.html  
is the entire map, not a cropped subset.

   1. Let me start with the disclaimer that I was in no way involved
      in this work.  There are others on the list who are at least
      close to this work if not directly involved.  I hope they will
      chime in...
   2. I don't think the goal of the project was to create this
      particular visual.  This particular visual (the whole one, not
      the cropped one in the article) is surely used mostly
      "iconically" to give the layman a sense of what the work is
      about.  Imagine if no such visual were included in the
      article...  even more opaque I think.  A list of how many
      articles and the major (conventional classifications they were
      in) and the number of links between the classifications seems
      like about as far as you could go, especially for a lay publication.
   3. To whatever extent the researchers use visualizations like this
      for Analysis, they probably use many...  with different
      thresholding criteria, different subsets, etc.   I myself,
      prefer a completely dynamic, interactive network layout for
      analysis.  In fact, I prefer one embedded in a 3D environment
      which I can explore more directly.
   4. In my work in SciViz, InfoViz, and Visual Analytics, I would
      claim that virtually none of the visualizations my colleagues
      use for doing analysis would be immediately useful to the casual
      observer.   Those which are not particularly abstract (fluid
      flows) or  very familiar (conventional charts and graphs) might
      be recognizable, but not necessarily useful.  How many people
      would know to look for or recognize a "bowtie" in a
      computational mesh?  How many would see that the adaptive
      meshing technique was failing in a region of high change?  Etc. 
      Even simple charts and graphs intended for analytical use are
      opaque to the layman.   So, I can tell that the concentration of
      a particular ion goes up roughly exponentially with one factor
      and more linearly with another... so what?
   5. Even Geography/Cartography can elicit a "so what"?   There are
      big deserts in along parts of the equator, rain forests along
      other parts, I bet it is hot there.  Mountains seem to come in
      long skinny ranges or big clumps.   Coastlines are ragged.   The
      names of countries in South America seem to be Spanish.  There
      are a lot of countries in Europe I never thought about because
      they were formerly lumped in with the Soviet Union.  Didn't I
      know all those things before I looked at a world map with
      geopolitical features marked?  Actually, I probably learned them
      from maps I have seen all my life.
   6. In my experience, especially with Visual Analytics, the goal is
      Exploration, Discovery and then maybe, sometimes Analysis. 
      Exploration and Discovery are a lot more "fun" even if the real
      work is in the Analysis.
   7. Network Science is not new, but it has only been about 10 years
      that it has become highly popular and widely used.  The visual
      (and linguistic) idioms are still somewhat young and we haven't
      all learned to read/think with them. 

Going to the actual network diagram...
    
http://www.nytimes.com/imagepages/2009/03/13/science/16visual-popup.html

Without knowing the key to the node size and colors...  I can intuit, 
or extract some interesting (to me) things.

   1. There are a few large clusters of relatively tightly coupled
      subjects which are relatively distinct from eachother.
         1. Soft Sciences, Religion, etc.
         2. Biology, Environmental Science, Ecology, Agriculture
         3. Hard Sciences, Physics, Chemistry, etc.
         4. Health Sciences
   2. The biggest "wad" are what some of us would call the "soft
      sciences".  It might not surprise some of us to notice that Law,
      and Education and Philosophy are fairly entertwined.  It *might*
      surprise some of us that statistics is so connected.  
   3. there is another "big wad that we might generally refer to as
      the hard sciences.  
   4. It might surprise some of us that Biology seems to be somewhat
      distinct from the other sciences, connected through
      biochemistry, toxicology and biotechnology.
   5. It might inform, if not surprise some of us to realize that
      Psychology might be tied to Biochemistry and that Biology ties
      to Architecture and Design through Biodiversity and Ecology.
   6. It surprises me that the wad on the left in Red which roughly
      seems to relate to Medicine in general, doesn't tie in with
      Physiology and Genetics from within the Biology Cluster.
   7. Does it surprise us that Statistics is tied to Medicine through
      Demographics and then through Clinical Trials?

It may be my experience and normal role, but an important thing I 
think I see in this visualization is that either the data or the 
tuning of the parameters might have artifacts.   This Visualization 
was probably not tuned for Analysis, or if it was, it was tuned for 
one aspect of the data.  It was probably tuned to make a pretty 
picture so folks who know nothing about what they are doing, would at 
least be able to see the rough structure and symmetry.  No criticism 
of their work here.

   1. Why is Pharmaceutical research disconnected from Clinical
      Pharmacology?
   2. Cognitive Science and Neurology?
   3. Where is Engineering?
   4. Why is Tourism there?
   5. What else is missing, obscured, or that I'm not noticing?

I immediately want to do several things:

   1. move this into 3D so there is more "conceptual layout space" and
      so I can adjust perspectives to see different otherwise occluded
      features.
   2. make it dynamic so that I can "pluck" portions of it and watch
      the disturbances propagate, adjust parameters and watch it evolve.
   3. play with the parameters to accentuate tight clusters or lightly
      connected subsets (this view is good that way).
   4. Select smaller subsets (zoom in on details).
   5. Interrogate specific  nodes for their details.
   6. Manually aggregate what my visual judgement suggests are
      "clusters", building a hypergraph.



And all this without really knowing what the data is and what they are 
really trying to show here.  The more I look at it, the more I get out 
of it (and the more questions I have).  Does anyone else have this 
experience?  Or is everyone else equally puzzled by this kind of "map"?

- Steve
PS.  Yes, Doug, I am avoiding a deadline, why else would I dive in so 
deep on this!
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============================================================
FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
lectures, archives, unsubscribe, maps at http://www.friam.org