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I am endlessly fascinated with content tagging systems

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Re: I am endlessly fascinated with content tagging systems

#111

Post by travisjungroth »

xg15 wrote: post #82

I worked with the Wikipedia category system a few years ago, and you could see the problems with hierarchical tagging systems right in action back then. (Though it may have gotten better in the meantime) The system appeared simple: There were just two relations, "Article A is a member of category B" and "Category X is a subcategory of category Y". However, in practice, the community was using this system to represent…

The issue is that system has nodes and edges, but no concept of distinct graphs. That leaves you trying to fit all notable human knowledge onto a single graph, which is non-optimal. Whether it’s also a DAG, tree, or something else doesn’t even matter.

Ontologies are like languages. There is no correct one. What matters is how good a fit it is for the problem at hand and that you’re all using the same one! If half the people are using Italian and half Spanish, it’s going to be a disaster. I wouldn’t use APL to write a UI and I wouldn’t architect a computer system in Shipibo.

Similarly, if I’m bird watching, “Birds of Northern California” is very useful. Organizing them by genus is less useful to me in that moment, but it’s not wrong.

Re: I am endlessly fascinated with content tagging systems

#112

Post by emj »

Openstreetmap is map data that is basically coordinates with tags on them and relations between those tags. I guess this is true for most GIS software but there is very little 2D map data that can not be described in the OSM tagging model.

You can never express everything with tags, you need stats and metadata on metadata, documentation and a strong heterogeneity which also need to be able to adapt to new ideas.

https://wiki.openstreetmap.org/wiki/Tags https://wiki.openstreetmap.org/wiki/Map_features

Re: I am endlessly fascinated with content tagging systems

#113

Post by roberthahn »

I'm so happy to see people talk about this! I too am endlessly fascinated with content tagging systems.

Hillel's thoughts are completely unsurprising to me so I guess I've come to similar conclusions.

I do notice that we seem to care about different things though - where Hillel appears to focus on tag types (and the implementation challenges that go with that) I focus more on human factors like what problem are we solving? for who? How do we maintain relevance (and power) in tagging systems (and for who?)

I'm of the opinion that tagging systems should not be made by the few for the many but by each person for themselves. Which, of course, sucks because that puts the onus on everyone who wants tagged content to do their own work. But I believe the output of that investment would be quite valuable and useful!

An easy example I could use might be recommendation engines. Assume I have a database of tags (a tag cloud?), and I know you have similar interests to me. If you also have a tag cloud, I could input links to both of our tag clouds into a purpose-built recommendation engine to discover new content I might not have consumed yet.

Re: I am endlessly fascinated with content tagging systems

#115

Post by maratc »

xg15 wrote: post #82

I worked with the Wikipedia category system a few years ago, and you could see the problems with hierarchical tagging systems right in action back then. (Though it may have gotten better in the meantime) The system appeared simple: There were just two relations, "Article A is a member of category B" and "Category X is a subcategory of category Y". However, in practice, the community was using this system to represent…

The problem might not be with hierarchical tagging systems, but with the specific hierarchical tagging system they use at Wikipedia.

Imagine another system with the following categories:

* People:ByOccupation:Creative:Writers

* Time:CommonEra:ByCentury:19

* Location:Earth:Americas:NorthAmerica:USA

In this scheme of things, e.g. Mark Twain would be tagged with all three. "19th century American writers" (which includes Mark Twain) would not be a category but a saved search. (Other saved searches — which would also include Mark Twain — would be "19th century people from Americas" or "Stuff from Planet Earth").

Re: I am endlessly fascinated with content tagging systems

#116

Post by hoherd »

openfuture wrote: post #78

Why twitter man.. these questions are clearly important but there is a space to discuss them https://matrix.to/#/#datalisp:matrix.org

Seriously. I'm not a twitter fan, but even so, it's a short-form medium. Why do people abuse it like this, especially with great content? What's so bad about tweeting a link to a blog?

Anyhow, I use threadereaderapp to get through the frustrating twitter UI and the ways that it is abused: https://threadreaderapp.com/thread/1534301374166474752.html

Re: I am endlessly fascinated with content tagging systems

#117

Post by at_a_remove »

PuppyTailWags wrote: post #108
at_a_remove wrote: post #67

Earlier quoted context omitted.

Then tags in AO3 are just more of the text and not much of a finding aid. You can't have both.

Tags end up being an excellent finding aid due to the strength of the community's tag linking, you see. So they serve both purposes.

"no regenerating limbs those arms are toast sorry QA despises them" just isn't useful if I want to locate a particular text, other than "I'm liable to get a Tumblr-stink off of this crap."

And your defense of this is really ... internal, as in, this all looks like a lot of in-jokes to an outsider who is new to AO3, or even new to a particular fandom. If someone doesn't know the slang, the in-joke reference, it's still unhelpful.

Re: I am endlessly fascinated with content tagging systems

#118

Post by crazygringo »

maxbond wrote: post #100
crazygringo wrote: post #96

Earlier quoted context omitted.

I encountered the same problem a few years ago and indeed realized that using categories to understand what type of article a thing was (person? subject? event?) was utterly useless, for the reasons you describe. On the other hand, I discovered that infoboxes (the data in the top-right box on most pages) was generally extremely reliable, if frustrating to parse.

The infoboxes are created from a query to Wikidata, which you can query yourself! No scraping necessary! https://query.wikidata.org/ You'll want to learn SPARQL, but if you know SQL it's not so bad to pick up.

As far as I can tell, that is not the case, sadly.

Right now it appears that only 3,975 articles have infoboxes auto-generated from Wikidata. [1] The wikitext contains something like "{{Wikidata Infobox ...}}" instead of just "{{Infobox ...}}".

If you look up a popular article like Barack Obama [2], it's just a traditional hand-edited infobox. In fact, one of the first lines of data says "Vice President = Joe Biden", while the Wikidata entry for Barack Obama [3] doesn't reference Biden anywhere -- so not only is the Wikipedia infobox not generated from Wikidata, but Wikidata isn't pulling all the relevant info from Wikipedia either.

Back when I had been working on my project, I'd hoped Wikidata could be a solution but it was far too incomplete and information was regularly out of date. Perhaps (hopefully) it's better now, but it's clearly not being used to power infoboxes yet except in a tiny number of cases. (Which actually complicates things more now, since anybody parsing Wikipedia infoboxes now has to deal separately with the 3,975 ones that grab from Wikidata, since none of the actual data is copied over into the wikitext...)

[1] https://en.wikipedia.org/wiki/Category:Articles_with_infobox...

[2] https://en.wikipedia.org/wiki/Barack_Obama

[3] https://www.wikidata.org/wiki/Q76

Re: I am endlessly fascinated with content tagging systems

#120

Post by josefrichter »

I was fascinated by ontologies 10 years ago. Since then, I've been studying human brain, only to realize that this is an effort to basically build a software version of human brain. Maybe it's possible, but it's definitely not feasible in 99.9% of cases. The closest thing we have is some machine learning approaches.
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