It's cute but what is it for?

[Robert Holmes]

Map of knowledge at 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? 

R

============================================================
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

[Nick Thompson]

Robert,

 

your wrote:

 

==>I have a hard time working out what these network visualizations are meant to be telling me. That academic disciplines are connected?<==

 

That psychologists are in deep center field.

 

Nick

 

Nicholas S. Thompson

Emeritus Professor of Psychology and Ethology,

Clark University ([hidden email])

http://home.earthlink.net/~nickthompson/naturaldesigns/

 

 

 

 

----- Original Message -----

From: [hidden email]

To: [hidden email]

Sent: 3/17/2009 6:14:58 PM

Subject: [FRIAM] It's cute but what is it for?

Map of knowledge at 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? 

R

============================================================
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

[Steve Smith]

Map of knowledge at 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!

============================================================
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

[James Steiner]

My day job is for the Scientific business of Thomson Reuters.

I'm pretty sure someone did something similar using citation indexes.
If only I could find the link.

~~James


On Wed, Mar 18, 2009 at 1:59 AM, Steve Smith <[hidden email]> wrote:
>
> Map of knowledge
> at http://www.nytimes.com/2009/03/16/science/16visuals.html?_r=2&emc=eta1 built
> by scientists from LANL, SFI etc.

============================================================
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

[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 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!

============================================================
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

============================================================
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

[Robert Holmes]

The journal article referenced in the NY times piece is at http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803 and is worth a read. Lots more pretty pictures. Also some intriguing stuff about how they built this map. Basically, it's from readers' clickstreams: this map is actually saying "these are the subjects that readers tend to look at during a single session in front of the computer". An interesting take on how academic disciplines are inter-related.

Also there's a note at the bottom of the article saying that the authors will make their data set available to anyone who wants it. So which of you visualizers is up for a weekend project? ;-)

Robert

On Tue, Mar 17, 2009 at 11:59 PM, Steve Smith <[hidden email]> wrote:

Map of knowledge at 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!

============================================================
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

============================================================
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

[Tom Johnson]

Great observations from both Steves.  But I would add that the map -- as helpful as it is -- strikes me as very English- and perhaps U.S. - centric.  For example, we have recently learned of the close connections in Latin America among those who coming from both physics and philosophy to study Complexity, especially as the latter is used to understand public health.  Such  links would seem to be absent here.  I wonder what will happen if the researchers can run the analysis using, say, Russian or Spanish?

-tj
=============================================================================================================

On Wed, Mar 18, 2009 at 7:01 AM, Stephen Thompson <[hidden email]> wrote:

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 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
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FRIAM Applied Complexity Group listserv
Meets Fridays 9a-11:30 at cafe at St. John's College
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==========================================
J. T. Johnson
Institute for Analytic Journalism -- Santa Fe, NM USA
www.analyticjournalism.com
505.577.6482(c)                                    505.473.9646(h)
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"You never change things by fighting the existing reality.
To change something, build a new model that makes the
existing model obsolete."
-- Buckminster Fuller
==========================================

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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