Development on the MOM SSC framework and especially implementing one core part of the reorganizer got lagged because I am still after getting a job (and other issues). Apparently, that search distracts more but actually having a job.
However, the time to read the feeds I defend. But there I found a problem -- too much interesting news and too many repetitions of the same topic. I survived one Apple keynote time, and I endured the Vista market introduction. But when there was another hype on the iPhone I begun feeling nagged.
Now, as the iPhone wave gets prolonged by iPhone hacks, and as noone can hid from that Harry Potter hype, I really get annoyed. -- As the Model of Meaning provides the logic to detect similarities, I want a tool that determines old news and variants of yet known news. Such as the latest iPhone hack or Potter p2p share.
Another way but looking up and dealing with the tags of feed entries, might be to take the words of any set of two or more articles and see for sets of words they share. A more brute-force (and less MOM way approach would be to take word neighbourhoods (word sequences) into consideration. -- On the other hand, the tool-to-be could use wordnet to include synonyms into 'consideration' when looking for similarities between texts.
For that reason, now I see how I can get through with the beforementioned reorganizer core -- the one that actually detects similarities for to save edges, i.e. storage -- logical by edges as well as "physically" by disk space.
Updates: 20070731: linked the word "lagged" to the last recent release posting
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