(continued from Part III)

Now that I know where the documents are located in the landscape, I have experimented with ways to estimate where the topic map topics are supposed to be. My hypothesis is that if I can determine the distance of each document to every topic, I can triangulate the topics.

Topic Positions

Below (larger version in the attachments) is a new rendering of the MapReduce theme:

It shows the themes derived from the semantic corpus (documents + semantic network). Compare this with the positions of topics:

There are two major additions:

• The first allows you to use a whole image stack as canvas for your image pyramid.

• The other provides you with a more convenient and shorter way where things are supposed to be stored.

Image Stacks

The amazing Image::Magick package can cope with images which are linked together:

If you resiz

(continued from HD Semantic Maps)

Like most of you, I collect bookmarks. But unlike most of you, I store them into a semantic network, a topic map to be precise.

One problem I certainly share with you, is that all these laboriously collected links are prone to break. To recover them sometimes needs considerable effort and - according to another Murphy Law (are there actually any other laws?) - always hits you at the most inappropriate time.