Topic Maps is an international standard (ISO 13250 – Topic Maps) and semantic technology for structuring of information and information resources. An often used analogy is that Topic Maps are to electronic information what back-of-the-book indexes are to traditional books. You may also compare it to a mind map; topics are inter-connected through relations (in TM called associations). And that’s about it as far as the model goes;

Topics, Associations and Occurrences (i.e. information about topics) are the main building blocks of Topic Maps. Alas, Topic Maps is not a technology for storing information, a competitor to RDBMSes or an XML vocabulary (although there is XML Topic Maps), nor did it’s authors intend for it to take over the role of (X)HTML. Instead, it is a semantic web technology in that it adds an abstract layer of meta data (information overlay) to the WWW (or whatever).

By creating topic maps that convey information about where to find various types of information, one might explore data sets in new ways. This might be utilized in order to:

• Arrange information in taxonomies or thesauri for classification purposes
• Create “intelligent” search engines.

Another often mentioned benefit is that since the Topic Map model is associative by nature, people can actually learn new things by exploring topics and associated topics (and associations). Whether this is actually true does of course not only depend on your definition of “learning”, but other factors as well, such as the actual contents of the topic map in question. Using a self describing data model is not enough.

That being said,

• Topic Maps might enable learning through exploration.

Resources

If you want to learn more about topic maps, these are some great resources:

• The TAO of Topic Maps by Steve Pepper.
• Metadata? Thesauri? Taxonomies? Topic Maps! by Lars Marius Garshol.

You may also want to take a look at TopicMap.com’s compilation of Topic Maps resources.