Intelligent Approaches to Mining the Primary Research Literature: Techniques, Systems, and Examples

In this chapter, we describe how creating knowledge bases from the primary biomedical literature is formally equivalent to the process of performing a literature review or a ‘research synthesis’. We describe a principled approach to partitioning the research literature according to the different types of experiments performed by researchers and how knowledge engineering approaches must be carefully employed to model knowledge from different types of experiment. The main body of the chapter is concerned with the use of text mining approaches to populate knowledge representations for different types of experiment. We provide a detailed example from neuroscience (based on anatomical tract-tracing experiments) and provide a detailed description of the methodology used to perform the text mining itself (based on the Conditional Random Fields model). Finally, we present data from textmining experiments that illustrate the use of these methods in a real example. This chapter is designed to act as an introduction to the field of biomedical text-mining for computer scientists who are unfamiliar with the way that biomedical research uses the literature.