  The majority of big data is unstructured and of this majority the largest
chunk is text. While data mining techniques are well developed and standardized
for structured, numerical data, the realm of unstructured data is still largely
unexplored. The general focus lies on information extraction, which attempts to
retrieve known information from text. The Holy Grail, however is knowledge
discovery, where machines are expected to unearth entirely new facts and
relations that were not previously known by any human expert. Indeed,
understanding the meaning of text is often considered as one of the main
characteristics of human intelligence. The ultimate goal of semantic artificial
intelligence is to devise software that can understand the meaning of free
text, at least in the practical sense of providing new, actionable information
condensed out of a body of documents. As a stepping stone on the road to this
vision I will introduce a totally new approach to drug research, namely that of
identifying relevant information by employing a self-organizing semantic engine
to text mine large repositories of biomedical research papers, a technique
pioneered by Merck with the InfoCodex software. I will describe the methodology
and a first successful experiment for the discovery of new biomarkers and
phenotypes for diabetes and obesity on the basis of PubMed abstracts, public
clinical trials and Merck internal documents. The reported approach shows much
promise and has potential to impact fundamentally pharmaceutical research as a
way to shorten time-to-market of novel drugs, and for early recognition of dead
ends.
