Building Queries - Information Trapping: Real-Time Research on the Web - page 84

he suggestions and the techniques I've gone over thus far are very useful
for all kinds of data collections, be they Web pages, news search engines,
article collections, sets of RSS feeds, or what have you. However, they are
not useful for two other kinds of information collections—tags and conver-
sations. For those you'll need to use a diferent approach.
Tags and Conversations
What are tags and conversations? Conversations, of course, are discussions
that might happen on mailing lists or on public forums. You can ind them
via general search engines, but there are also many specialty search engines
that index only conversations.
Tags you might not know about. A tag is a keyword that someone can use
to describe a resource in a directory. Usually a search engine or directory
that indexes tags has many people “tagging” resources at the same time. he
index of words used to describe the contents of a directory built that way is
called a folksonomy , a taxonomy developed by a group of people. Tags are
not full descriptions or site titles—usually they're just a word or two.
tIp
You can learn a lot more about tagging and folksonomies at
Wikipedia, including an overview on folksonomy at en.wikipedia.
org/wiki/Folksonomy.
You will need to change your strategy when developing monitors for tags
and conversations. Why? he big answer is language.
In the case of tags, you're not searching for summaries or even fully articu-
lated concepts. Instead you're searching for a word or two words. In the case
of conversations, you're looking for much more informal language, such as
sentence fragments and scraps of conversations.
hese kinds of data pools are very diferent from a structured, organized
Web page. And for that reason you'll have to use diferent strategies for
monitoring them. Later in this topic, we look at where you can go to search
and monitor tags and conversations, but here we're going to stick with the
idea of queries and how to create them.
For now, let's take a look at searching with tags.