Abstract: Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological
knowledge is worldwide represented in a network of databases. These data are spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail,
interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in
information retrieval environments an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of
providing a comprehensive knowledge excerpt out of the interlinked databases. A prerequisit for supporting the concept of an integrated data view is the to acquiring insights into cross-references
among database entities. But only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend
an automated construction of an integrated data network is possible. We propose a method that predict and extracts cross-references from multiple life science databases and thier possible
referenced data targets. We study the retrieval quality of our method and the relationship between manually crafted relevance ranking and relevance ranking based on cross-references, and report on
first, promising results.