Research results are primarily published in scientific literature and curation 
efforts cannot keep up with the rapid growth of published literature. The 
plethora of knowledge remains hidden in large text repositories like MEDLINE. 
Consequently, life scientists have to spend a great amount of time searching for 
specific information. The enormous ambiguity among most names of biomedical 
objects such as genes, chemicals and diseases often produces too large and 
unspecific search results. We present GeneView, a semantic search engine for 
biomedical knowledge. GeneView is built upon a comprehensively annotated version 
of PubMed abstracts and openly available PubMed Central full texts. This 
semi-structured representation of biomedical texts enables a number of features 
extending classical search engines. For instance, users may search for entities 
using unique database identifiers or they may rank documents by the number of 
specific mentions they contain. Annotation is performed by a multitude of 
state-of-the-art text-mining tools for recognizing mentions from 10 entity 
classes and for identifying protein-protein interactions. GeneView currently 
contains annotations for >194 million entities from 10 classes for ∼21 million 
citations with 271,000 full text bodies. GeneView can be searched at 
http://bc3.informatik.hu-berlin.de/.