Like it says.
Besides the already mentioned goal of allowing software to 'understand' the data, there are more practical applications in using it to translate between ontologies, or for mapping between dis-similar representations of data - without having to translate or standardize the data (which can result in a loss of information, and typically prevents you from improving your understanding in the future).
There were at least 2 sessions at OSCon this year related to the use of semantic technologies. One was on BigData (slides are available here: http://en.oreilly.com/oscon2008/public/schedule/proceedings, the other was the guys from FreeBase.
BigData was using it to map between two dis-similar data models (including the use of query languages which were specifically created for working with semantic data sets). FreeBase is mapping between different data sets and then performing further analysis to derive meaning across those data sets.
Related topics to look into: OWL, OQL, SPARQL, Franz (AllegroGraph, RacerPRO and TopBraid).