# Tags and AI or Machine Learning

**URL:** <https://discourse.devontechnologies.com/t/tags-and-ai-or-machine-learning/50123>\
**Category:** DEVONthink\
**Created:** [September 18, 2019, 1:41am UTC](https://discourse.devontechnologies.com/t/tags-and-ai-or-machine-learning/50123 "2019-09-18T01:41:42Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![ngan](https://discourse.devontechnologies.com/letter_avatar_proxy/v4/letter/n/4da419/32.png) [@ngan](https://discourse.devontechnologies.com/u/ngan)\
**Post date:** [September 18, 2019, 3:29am UTC](https://discourse.devontechnologies.com/t/tags-and-ai-or-machine-learning/50123/4 "2019-09-18T03:29:46Z")

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> [@BlueKnight](#):
>
> the software can utilize the AI engine to see the similar × items and see the tags that are currently used and recommend them.

A primitive attempt here: [Tagger V2. Two unique features, half-useful, but still interesting (I think)](https://discourse.devontechnologies.com/t/tagger-v2-two-unique-features-half-useful-but-still-interesting-i-think/49744).  
I’m neither a programmer nor have knowledge in AI or ML. I am just trying to use the “see-also” to do the heavy lifting work. I think whether auto tagging can be successfully done in DT is depending on (1) the objective/nature of tagging, (2) the word count of the items within the database, (3) consistency of existing groups, (4) or as simple as naming of the file.  
A very systemic naming system can achieve high quality auto-tagging simply by word match.  
See-also tends to give relatively good quality of common-tags-based suggestion.  
A database with very consistent and distinctive groups in the database may/will help to narrow down a more targeted set of common tags (only extract the common tags from those see-also documents that are within the higher scored classify-to group).  
Unless frequency count is the main criteria of tagging, I think concordance is too “raw” to be used for perception- or interpretation-based tagging because concordance in itself doesn’t constructed schemata. See-also and classify are the processed products of concordance so they may be more useful.

Just a very non-professional reading of auto-tagging, so u need to forgive my naive comment!

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