The phenomenon of machine tags
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Tags in general are one of most recognized Web 2.0 products. Going one step forward, one can say that machine tags as a part of the semantic web, should become one of the most recognized Web 3.0 products. Before we try to understand usages of machine tags, first we should understand what the meanings of the tags / tagging are, what kinds of tags exists and how users of internet can exploit it.
There is not official definition of tags and tagging but there are several characteristics of the tags that are applicable to all tag. Tags are user contributed (user-generated) descriptive strings, possibly labels and keywords that are describing a piece of content. Those strings should be relevant and easily associated to the piece of content. Under the content we can understand URLs, web pages, texts, images, videos, geographic maps, blog entries etc. Tags are not same as keyword annotations. The difference is that tags are flat, disorganized, free-form strings made by users and keyword annotations are usually part of the predefined vocabulary given by different authors, web systems (web sites, web directories, web platforms etc.) or librarians.
The fact that the tags are made by humans according to their own understanding of the content can be advantage and disadvantage of the tags systems. It is advantage in the sense that user knows and understands meanings of the content (data) and by adding the tags he can easier remember, retrieve, recognize, save, browse and search for content. The major disadvantage is that the same content can be tagged differently by different people. For example, images on the Flickr could be tagged according to the place where they had been taken (geo-tags) or by its content. If we have image of the mountain we can tag it with: “winter” (time of year when image had been taken), “Zlatibor” (place), “skiing” (activity shown on the image). But same image could be tagged also with: “January” (winter month), “Obudojevica” (skiing resort on Zlatibor), “skiing”. Another problem of tagging systems is that “system” doesn’t understand meaning of the tags. For example, tag “java” can describe computer company and program, coffee and island; tag “apple” can be applicable for both computer company (Apple Inc.) and fruit. In the case of individual tagging on the personal computer, those problems are not crucial, but in the case of collaborative / sharing tagging systems (like: delicious.com, flicker.com, digg.com) those problems are critical.
In social bookmarking web sites (collaborative tagging communities), users can share tags one with another, retrieve tagged content online, search, browse and filter tags. Examples of such communities are: Delicious, Flickr, Digg etc. We can distinguish social bookmarking communities according to the type of the content they are used to tag:
- Tagging for URL (for example: del.icio.us, stumbleupon.com)
- Tagging for photos (for example: flickr.com)
- Tagging for videos (for example: youtube.com)
- Tagging for news (dig.com, reddit.com, netscape.com)
- Tagging for books (librarything.com, openlibrar.com)
- Tagging for academic articles (citeulike.com)
- Tagging for retail products (amazon.com)
Those entire collaborative tagging systems share previously described problems. Some of them try to resolve it by using the machine tags.