Thank you for the examples. I agree, we need to think about the question especially for representation of complex answers.

To Stefa:

Thank you for the comments, and recommendations. The book is very good and relevant. I like the quotation “Visualizing Data teaches you how to answer questions, not simply display information” from the book very much. Since you mentioned “granular”, yes, in LarKC we do consider knowledge representation and reasoning with multi-level of granularities and multi-views. Some preliminary introduction can be found in http://wiki.larkc.eu/GranularReasoning.

To Valentin:

Special appreciation should be given to you since in this way, we can come to deeper discussion:). I learned a lot through reading your blog, it is great, and I should learn more from you:)

I think there is no doubt that (sometimes) graph can help to understand. Considering general purpose visualization tools, for “Big Fat Graphs” that shows everything, I think we can avoid over-utilization by showing results with multi-level of importance (in a rational manner) with user intervention. If we add limited rationality to visualization, appropriate utilization might be produced. Rational Choice is one of the core spirits of LarKC that brings from human problem solving to the Semantic Web.

For an excellent example of how to deal systematically with this kind of issues (in a truly outstanding visualisation environment) I would recommend
Ben Fry’s Visualizing Data: Exploring and Explaining Data with the Processing Environment .

In Europe we have some oustanding talent in this domain, among them Moritz Stefaner from Potsdam.

Here’s a little mashup using zemanta, dbpedia, freebase and graphviz: http://test.infoblow.zemanta.com/infoblow/galaxy/

basic, but still much better than nothing.

bye
Andraz Tori, Zemanta