  Normalized web distance (NWD) is a similarity or normalized semantic distance
based on the World Wide Web or another large electronic database, for instance
Wikipedia, and a search engine that returns reliable aggregate page counts. For
sets of search terms the NWD gives a common similarity (common semantics) on a
scale from 0 (identical) to 1 (completely different). The NWD approximates the
similarity of members of a set according to all (upper semi)computable
properties. We develop the theory and give applications of classifying using
Amazon, Wikipedia, and the NCBI website from the National Institutes of Health.
The last gives new correlations between health hazards. A restriction of the
NWD to a set of two yields the earlier normalized google distance (NGD) but no
combination of the NGD's of pairs in a set can extract the information the NWD
extracts from the set. The NWD enables a new contextual (different databases)
learning approachbased on Kolmogorov complexity theory that incorporates
knowledge from these databases.
