<snip> ... It’s been fascinating to see how Joe thinks about TiddlyWiki, and encouraging to have some mainstream interest.
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The so mominated technical items, are sourced from technical diciplins, however the relavant learnings to dealing with data, information and knowledge are not dissimilar to understanding analysis and synthesis. They include such concepts that are so importiant they should be understood publicaly.
One example is "normalisation" from database design and management. The pithy statement that comes from that is the details in a given record should be related to the key, the whole key, and nothing but the key.
Perhaps we could say that every field including the text field in a tiddller should be related to the tiddler title, the whole title and nothing but the title.
Understanding this could fix a lot of speadsheets out there or make obviouse, common logical errors.
Regards
Tony
... Perhaps we could say that every field including the text field in a tiddller should be related to the tiddler title, the whole title and nothing but the title ...
Ciao TonyThanks for this ...
TonyM wrote:... Perhaps we could say that every field including the text field in a tiddller should be related to the tiddler title, the whole title and nothing but the title ...
Its interesting both the style of tagging and its various emoluments (profits). But the "title" is already a kind of default tag.TW is interesting because its tags serve several functions (semantic, organizational, systemic) seamlessly.
But, at the same time, any TW tag is a "label applied" to a tiddler -- a distance between the tiddler and its manifest content.FYI I'm a big fan of Twiitter where #hashtags are always inline. No separation of content from organization. Its a neat approach on content cognisance. Twitter is maybe extreme in its #hashtaggery but its effective in terms of finding stuff well enough. But, of course, Twitter usage of #hashtags is purely about flagging content, whilst in TW tags do several jobs.
--Just commentsJosiah
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What I did was to use Baysian inference
...This way I could correctly predict about 80% of the tags from the text alone.
The problem was that, to me, many of the tags were meaningless and were used internally to organise the TW.
In a second experiment I totally ignored the assigned tags, and predicted the tags froma TF*IDF analysis of the text. This made tags that made much more sence to me, but thepredicted tags often missed the supplied tags.
In my opinion the TF*IDF were better than the assigned tags since they had nothingto do with the organisation, but more to do with the actual words in the text.
But, at the same time, any TW tag is a "label applied" to a tiddler -- a distance between the tiddler and its manifest content.FYI I'm a big fan of Twiitter where #hashtags are always inline. No separation of content from organization. Its a neat approach on content cognisance. Twitter is maybe extreme in its #hashtaggery but its effective in terms of finding stuff well enough. But, of course, Twitter usage of #hashtags is purely about flagging content, whilst in TW tags do several jobs.YES :-) -- Given my earlier observations, perhapse we could distinguish two types oftags. The #inlineHashTags could have something to do with the content of the containing paragraph. The tiddler tags could mean "tags used to internally organise the TW itself"
If you really like the ease of setting and removing tags you can also use Mario's alt-tags to have multiple tag fields. Such tag fields can be used as subject or category organisation
What I did was to use Baysian inference to "learn" the relationship between the words in the text and the supplied tags - so for each word in the text I caculate the probability that the tiddler has tag <T> (forall known tags <T>) - then in a second pass I tested the model and predicted the tags from the text. This way I could correctly predict about 80% of the tags from the text alone. The problem was that, to me, many of the tags were meaningless and were used internally to organise the TW.
In my opinion the TF*IDF were better than the assigned tags since they had nothingto do with the organisation, but more to do with the actual words in the text.
WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser. WordNet is also freely and publicly available for download. WordNet's structure makes it a useful tool for computational linguistics and natural language processing.
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For me it would be very interesting to have a mechanism, that would suggest "meaningful tags" by analysing the prose text(s). ... but it needs to work without a 3rd party server. It should be integrated into TW. ... Is this possible?
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Thinking out loud here ...I've been thinking more about tags. One problem is that tags are rather vague and are written in different human languages.One way out of this might be to adopt the wikidata word definitions. For example, I am, unambiguouslyThere are actually several Joe Armstrong's (for example, https://www.wikidata.org/wiki/Q712592)These Q numbers uniquely define subjects and objects. Verbs (or predicates) are given by P numbersso https://www.wikidata.org/wiki/Property:P178 means "the organisation or person who developed the item.
in RDF speak the triple(BTW I recommend clicking on these links and playing around - there's lots of interestingdata in RDF tuples and the above links are a good place to start looking)Means "TiddlyWiki developer Jeremy Rushton"These triples encode facts in a hopefully reasonably clear manner.So now the N$ question - can we automatically analyse a tiddler and turn it into a setof RDF tuples. If we could then we could add these to the huge databases of RDF tuplesand possible find stuff in a clever way.
The filter notation in the tiddlywiki reminds me very much of prolog, and I guess with a but ofwork SPARQL queries might be possible (SPARQL is an RDF query language)
Cheers/Joe
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