Derivation of implicit information from spatial data sets with data mining

Geographical data sets contain a huge amount of information about spatial phenomena. The exploitation of this knowledge with the aim to make it usable in an internet search engine is one of the goals of the EU-funded project SPIRIT. This project deals with spatially related information retrieval in the internet and the development of a search engine, which includes the spatial aspect of queries. Existing metadata as provided by the standard ISO/DIS 19115 only give fractional information about the substantial content of a data set. Most of the time, the enrichment with metadata has to be done manually, which results in this information being present rarely. Further, the given metadata does not contain implicit information. This implicit information does not exist on the level of pure geographical features, but on the level of the relationships between the features, their extent, density, frequency, neighbourhood, uniqueness and more. This knowledge often is well known by humans with their background information, however it has to be made explicit for the computer. The first part of the paper describes the automatic extraction of classical metadata from data sets. The second part describes concepts of information retrieval from geographical data sets. This part deals with the setup of rules to derive useful implicit information. We describe possible implementations of data mining algorithms.