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Tag improvements: hierarchy, relation and fuzzy mode

Tags represent the new borders for IA. They can mark contents in the user’s mental model better than taxonomies, and they can classify information for better research.

Several improvements can be studied for this field. This Microsoft research (2010) contains a lots of tips to improve the tag classification for a better IA.

The risk is the user experience: new patterns have to be designed for these representations.

Very interesting tagging application can be studied, developed and checked to define the best design for the users.

Hierarchical tags

They can be used for an IA that include vaste topics. They are better with arguments that can be cross but more detailed, too.

In a website, for example, we can have “disabilities” like a macro tag, that can involved in several type of news (politics, sport, and so on), and several subtopics like “Attention-Deficit”, “blindness”, and so on.

The Microsoft research demonstrates how users can find relevant tags more quickly in a hierarchical view rather than cloud. This is encouraging to study in deep this tagging mode.

Tags in a fuzzy mode

Tags can be not so flat. They can be involved in a contents with several levels. Every information contains a mix of arguments that are not used in the same “quantity”.

They can show different “tag rank” about an information.
For example, in a magazine website, we could have an article on Healty food with these tags and these rank:
health (++), food (+++), diet (+), and so on.

The rank indicates the topic presence and importance in the article.
It can be useful for the user to know how a topic is described, and it can be improve the research, too.

Imagine a search when user can “dose” topics like a recipe. The same user, finally, could assign a tag rank to a content, making the tagging auto-consistent.

Related tags

Tags can create relations very useful for the user. Like a related topic, system can measure the number of occurrence of the relation between tags in IA. With this information, we have an idea about the proximity of the arguments, and the users, too.

For example, in a IA node we can have a tag “environment”. This tag in the entire IA is frequently used with other tags, for example “ecology”. Tough in the single article is not applied “ecology” (but only “environment”), system could suggest it, and user could continuing browsing contents in a consistency way.

We can represent these connection using a wonder-weel that shows the relation with the proximity, like the Google one.

This is only one of the solutions for these news connection, but all have to be done!

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