text/editor auto-completion as a possible real world application for MOM

Right now, I am using my secondary workplace PC. At this one, I am used to use it one-handedly. And let the auto-completion kick in.

In a recent blog posting somewhere else, I was discussing lectures, lecturers, discussing as a topic, and the next issue I moved to was seminars. Intuitively, I expected, the auto-completion would kick in and offer "seminars" -- which it didn't.

I pondered whether to file a feature request, suggesting to background-use a thesaurus -- a word-processing one, not necessarily a real one -- to predict the words one might most-likely use soon. -- Then, I nticed, traditional term ordering systems like e.g. thesauri might have a hard time to do so; even more the programmers who actually should implement such kind of tool... well, on the second glance, maybe brute force could help there, and as a text is a relatively small amount of data (and vocabularies even more small), might be doable, easily to implement.

The brute force approach could pick up, stem the words of the text, then follow all the relation edges of a term to its set of neighbours, collect them, order them by alphabet, consider them like the words appearing really i the text: offer them for auto-completion where it looks appropriately.

On the other hand, a MOM approach might be to consider the words of the already typed-in text, step back a step, see the features of the items of the terms, count which other item(s) count the most features the until-now mentioned ones feature too. That way, we additionally would get a ranking of probability of upcoming terms. ... I'd do that myself, but the issue on tasks like this remans the old one: Where to get such interrelated collections of words in a reasonable amount and for reasonable .. no cost at all?

      
Updates: none so far

cluster your feed news: MOM reorganizer vs RSS feed news

The chance to reveal topics several different sources work with by applying a reorganizer also implies the chance to cluster RSS feeds by topic: Instead of approaching that issue by applying traditional information science procedures, alternatively the tags of the fetched articles could get looked up (retrieve the original article, pick its tags) and thrown through a reorganizer.

That might ease to skip feed news of usually valuable feeds on topics completely out of interest.

      
Updates: none so far

MOM's reorganization could reveal topics/theme complexes

Currently, I am preparing to implement MOM's reorganization capabilities. Today, some time amidst of it, I noticed that MOM could provide service already with only the reorganization functionality in place: Based on popular tagging, MOM [actually, its reorganizer] could reveal topics different sources (e.g. flickr photos or blog entries) deal with, unrecognizedly so far. -- The background:

Problem:
I've got lots of papers which are tagged. They deal with several different topics, on and off over time. There might be far later papers dealing with similar topics like any far earlier ones. -- Using the tags alone, doing that task intellectually, I might have a rather hard time: There are too many distinct tags to keep track of.

Approach for solution:
Reorganization could be applied: It might detect clouds of tags that belong together and 'mark' them by pooling them to separate new -- but yet unnamed -- 'tags' (= MOM nodes). That new tag, then, points to every paper the topic the tag represents deals with. -- That reduces the workload to be performed intellectually to find appropriate names for the newly created, first unnamed, tags. And, of course, to tag all those papers beforehand.

Benefit:
That does not only apply for my private issue of getting clear what topic I touched with MOM at what time, but also to any other unordered collection -- e.g. for papers collected in preparation of a diploma thesis..any scientific work, maybe even a law library..any library..all literature ever written.

      
Updates: none so far