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 <[email protected]>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! > > ------------------------------ > > ============================================================ > 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 > -- ========================================== J. T. Johnson Institute for Analytic Journalism -- Santa Fe, NM USA www.analyticjournalism.com 505.577.6482(c) 505.473.9646(h) http://www.jtjohnson.com [email protected] "You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete." -- Buckminster Fuller ==========================================
============================================================ 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
