  Digital Rights Management (DRM) prevents end-users from using content in a
manner inconsistent with its creator's wishes. The license describing these
use-conditions typically accompanies the content as its metadata. A resulting
problem is that the license and the content can get separated and lose track of
each other. The best metadata have two distinct qualities--they are created
automatically without user intervention, and they are embedded within the data
that they describe. If licenses are also created and transported this way, data
will always have licenses, and the licenses will be readily examinable. When
two or more datasets are combined, a new dataset, and with it a new license,
are created. This new license is a function of the licenses of the component
datasets and any additional conditions that the person combining the datasets
might want to impose. Following the notion of a data-purpose algebra, we model
this phenomenon by interpreting the transfer and conjunction of data as
inducing an algebraic operation on the corresponding licenses. When a dataset
passes from one source to the next its license is transformed in a
deterministic way, and similarly when datasets are combined the associated
licenses are combined in a non-trivial algebraic manner. Modern,
computer-savvy, licensing regimes such as Creative Commons allow writing the
license in a special kind of language called Creative Commons Rights Expression
Language (ccREL). ccREL allows creating and embedding the license using RDFa
utilizing XHTML. This is preferred over DRM which includes the rights in a
binary file completely opaque to nearly all users. The colocation of metadata
with human-visible XHTML makes the license more transparent. In this paper we
describe a methodology for creating and embedding licenses in geographic data
utilizing ccREL, and programmatically examining embedded licenses in component
data...
