You might look into Topic Modeling, or Topological Data Analysis. The basic idea is to build a database of entries and lists of words they contain, then run the data through a machine learning algorithm, which groups the entries into “topics”, and generate a page for each topic listing the entries that belong to the topic. Then you can add a toolbar to the bottom of each entry containing lists to all the topics that entry belongs to.
The algorithms have been reduced to black boxes, and there are tutorials for the black boxes. The difficult part is preparing the data. I’ve been wanting to do something like this for a while. I use Zim, a programmable desktop wiki. My problem is that my pages are full of markup, some of it generated programmatically, in order to make the wiki easier for me to use. All of the markup has to be removed before feeding the data into the black box.
You might look into Topic Modeling, or Topological Data Analysis. The basic idea is to build a database of entries and lists of words they contain, then run the data through a machine learning algorithm, which groups the entries into “topics”, and generate a page for each topic listing the entries that belong to the topic. Then you can add a toolbar to the bottom of each entry containing lists to all the topics that entry belongs to.
The algorithms have been reduced to black boxes, and there are tutorials for the black boxes. The difficult part is preparing the data. I’ve been wanting to do something like this for a while. I use Zim, a programmable desktop wiki. My problem is that my pages are full of markup, some of it generated programmatically, in order to make the wiki easier for me to use. All of the markup has to be removed before feeding the data into the black box.