As a result, Faceted systems essentially disintegrate into Tagsonomies. But...

• Loss of depth
• Because Facets are onerous to add and hard to design well, what most people end up with is more of a Tagsonomy: attribute values without attributes or a-semantic labels, which ultimately means, even less chunking, less structure and less narrative
  • Gmail labels are tags
  • http://del.icio.us
  • http://flickr.com
• In the end, Tags start to feel just as overwhelming as the free-for-all hierarchy of semantically inconsistent or worse, a-semantic, generic "Categories"

• Tags multiply like rabbits!
• The Tags multiply like rabbits The loss of dimensions in Tagsonomies can be thought of conversely as an unchecked multiplication of dimensions. Rather than thinking of Tags as all belonging to the same facet, the Tag facet, you can think of each individual Tag as its own dimension. See facetious. But, who can wrap their head around n-dimensions? n-dimensions eventually meld together, back into 1 dimension, just as the 100-sided volume (zocchihedrom) melds into a sphere with a continuous-surface.
• zocchihedron.jpg:
  

• Tags make items look like they're multiplying like rabbits Furthermore, the ability to assign more than 1 tag to an item, while more convenient when you're tagging, becomes a cognitive quagmire when it comes time to get a grip on the scope of your data. The ramplant multiplication of items showing up in multiple tag-groups can make a mountain out of a mole hill of data.
• As you browse around, jumping from tag to overlapping tag:
  • Sometimes you see the same items reappear for the nth time,
  • Occasionally you see new items appear for the first time, but you never get the satisfying feeling of knowing:
    • Where you are
    • What you've seen, what you haven't seen
    • How much of the data you've looked at and how much there is to go...this "sense of place" can only come from an orderly walk through an orderly tree (whether it's a fixed Hierarchy or a flexible tree generated by a Faceted browser).

Tags don't actually help you understand your data better, they're just a more usable way of labeling than the alternative of dragging and dropping items into folders

• Tagsonomies lack a visualization UI
• The sense of disorientation in Tagsonomies is partially because browsing tags is only one step above the "stick-your-hand-into-a-bag-experience described in the article IsSearchThePanacea?
• In comparison, hierarchies visualize the relationship between Containers. So if you're looking in the wrong Container, you can at least look at a neighboring Container to see if you've missed something. In other words, hierarchies visualize degrees of separation. (ie. Folder A is not inside Folder B, but it's next to Folder B, or it's 1 branch over at the same level as Folder B).
• There is no comparable visualization UI for Tags. As a result, in Tagsonomies, all neighbors are created equal. If what you're looking for doesn't exist in the tag or the intersection of tags you're currently looking at, you're out of luck. For any tag A, you can only guess which tags B,C,....or Y might be a bridge to another not directly related, but still highly relevant tag Z.
• Without a visualization tool, tags are just as dumb if not dumber than hierarchies. They also only have 2 kinds of relationships: instead of Parent-Child and Sibling, tags are either Related or Not related.

• chunking.png:
  

• The drawing above is one possible way to visualize faceted classification systems and/or tagsonomies
• Items are chunked into tags, categories or attribute value groupings: circles
• Attribute values are chunked into attributes or facets: same hue
• Attributes or facets are then chunked into hue families
• Visualizing the overlap also gives you a more textured narrative of the different kinds of relationships. All of a sudden, you go from a binary world view: related, not related to a much more nuanced universe of relationships between tags:
  • Degree of overlap with other tags
  • # of other tags
  • Relative size of this tag to other tags
  • Relative size of this tag to overlapping tags
• However, even this is problematic, but that's a different discussion. See the PrinciplesOfGrok series of articles.

Easy come, Easy go: Tagsonomies are too flexible for their own good

[However...Tags are great at Targeted search and retrieval...Sort of]

• Tagsonomies make it even easier than Faceted systems to label items, primarily because you don't have to worry about assigning the right Tag to the right Facet. Instead, you just blurt out free-form, stream-of-consciousness
• However, it's greatest strength is also it's greatest weakness. It's very flexibility can paralyze people as well.
• Some of the MIT Haystack studies asked users to "tag" URLs they found on the web with keywords as an alternative to filing bookmarks in folders. In the beginning, users felt great about the new paradigm. However, pretty quickly, many of the users began to feel like the whole process pointless. What often happens is that someone who is researching a particular topic (ie. Mating behavior of Bonobo apes) will find that all of the keywords they come up with apply to all of the material they find. As a result, they find it pointless to apply the keywords after a while.
• You could say that the users were just bad at coming up with good keywords, keywords that could actually help them differentiate between data, generate an unique enough metadata thumbprint of the item, rather than glom all of their data into one homogeneous mass. Another way of looking at it is that if the users had worked with a Faceted system instead, the structure inherent in the Faceted system might have guided them to attach the right kind of metadata to their content. Rather than simply coming up with subject matter or topical keywords, a Faceted system might suggest a richer variety of orthogonal or independent attributes to label items with such as: Content type, Pro v. Con, Status, Author background, Region, Time period, etc.
• Footnote: Another way to alleviate the seeming random pointlessness of applying keywords to items would be the ability to rank order the keywords. While it is certainly easy to imagine that a lot of content is about very similar if not the same set of topics, different content will often emphasize different areas of the same set topics. So out of 10 articles about Bonobo mating behavior, some might focus more on Courting in Mature apes and less on Sexual play in Juveniles). This would be yet another way to differentiate seemingly homogeneous sets of data.

Tags are too generic.

• Tags are too generic.
• The notion of "related tags" is too generic.
• Tags are unable to store important metadata about both our data and the relationships that govern and structure that data. As a result, Tagged data sets quickly explode beyond human ability to extract narrative and scope from the data.

Both beg the questions:

In what way is this tag relevant? Is San Francisco a location? the subject matter? the title? a saint?

How are these two tags related? In what way are these two tags related? Peers that overlap? Facets that intersect? Parent-child? Thread?

Scenarios

• Economics and Sociology are siblings that overlap: both are Areas of study or Disciplines.
• Modern, Occidental, Fine arts are independent facets that intersect.
• Feet and Toes are a variant strain of Parent-child relationships. All things that have Toes also have Feet, but all things that have Feet, don't necessarily have Toes.

"Big Announcement" and "re: Big Announcement" is an example of yet another type of relationship that is not Facets, Siblings or Parent-child though it is usually modeled as a Parent-child relationship. It is a thread relationship where "re: Big Announcement" is not some sub-area of "Big Announcement", but a response to it. In threads, ordering is of primary importance, but the items themselves are on equal footing, more siblings rivalry, less filial devotion.

Differentiating between the relationships between items is as important as differentiating between the items themselves. It is yet another way to help people identify and patterns in the data so that they can chunk it down into the 5-6 groupings they need in order to grok the information.

The "isRelated" generic relationship between overlapping Tags fails to map to real world realities. Again, by genericizing the data model, there is a loss of metadata, a loss of the semantic description of how two Tags are related to each other.

In the "real world" there is no such thing as the generic "isRelated" relationship. Whenever humans talk about relationiships, there is always some context to the relationship to make it comprehensible. It's only when we're "fuzzy" about why 2 things are related that we resort to generic descriptions: "I don't know why, but in my gut, I feel like these two things have something to do with each other."

So, a "fuzzy" understanding of their data is what we all generally expect and get out of software that presents data relationships to us as generic "isRelated" relationships that are agnostic to relationship type. And sometimes "fuzzy" quickly turns into overwhelming or incomprehensible or "I give up even trying to really understand this, I'll just click around and see what I end up with."

At which point, navigating Tagsonomies disintegrate into little more than human-aided search and serendipitous exploration. What it isn't is a self-sufficient organizational paradigm.